WO2013002381A1

WO2013002381A1 - Method for evaluating fatty liver disease, device for evaluating fatty liver disease, method for evaluating fatty liver disease, program for evaluating fatty liver disease, system for evaluating fatty liver disease, information-communication terminal device, and method for searching for substance used to prevent or improve fatty-liver-disease

Info

Publication number: WO2013002381A1
Application number: PCT/JP2012/066739
Authority: WO
Inventors: 三雄高橋; 山本　浩史; 文彦高月; 敏彦安東; 實山門
Original assignee: 味の素株式会社
Priority date: 2011-06-30
Filing date: 2012-06-29
Publication date: 2013-01-03
Also published as: JP6260275B2; JPWO2013002381A1; US20140113378A1; US9971866B2

Abstract

The present invention addresses the problem of providing a method or the like for evaluating fatty liver disease, capable of evaluating with high precision the state of fatty liver disease including at least one of fatty liver, NAFLD, or NASH, using the concentration of amino acid in the blood. According to this method for evaluating fatty liver disease, amino-acid concentration data related to an amino-acid concentration value in blood collected from an evaluation subject is acquired, and the state of fatty liver disease including at least one of fatty liver, NAFLD, and NASH is evaluated for an evaluation subject on the basis of the acquired amino-acid concentration data.

Description

Fatty liver disease evaluation method, fatty liver disease evaluation device, fatty liver disease evaluation method, fatty liver disease evaluation program, fatty liver disease evaluation system, information communication terminal device, and prevention and improvement of fatty liver disease Substance search method

The present invention relates to a method for evaluating fatty liver disease, a fatty liver disease evaluation apparatus, a fatty liver disease evaluation method, a fatty liver disease evaluation program, using an amino acid concentration in blood (including plasma, serum, etc.), Fatty liver disease evaluation system, information communication terminal device, and a method for searching for a substance for preventing or improving fatty liver disease that searches for a substance that prevents fatty liver disease or improves the state of fatty liver disease is there.

NASH (non-alcoholic steatohepatitis: non-alcoholic fatty liver disease) and NAFLD (non-alcoholic fatty liver disease: non-alcoholic fatty liver disease) are generally diagnosed by ultrasonography in fatty liver (liver cells). Based on the accumulation of fat droplets), liver tissue with liver history similar to alcoholic liver injury, despite no history of alcohol consumption and non-infection with hepatitis virus Yes (Non-Patent Document 1). Liver damage mainly characterized by large droplets of liver fat is called NAFLD. NAFLD also has a simple fatty liver with a good prognosis and progressive and eventually cirrhosis. It is classified as NASH (Non-Patent Document 1).

Historically, Ludwig reported in 1980 as NASH a patient who had no alcohol history but had alcoholic liver damage and led to cirrhosis, and in 1986 Schaffner reported a group of NAFLD diseases. . Regarding the liver histology of NAFLD, Matteoni type 4 classification (type 1: simple fatty liver, type 2: steatohepatitis, type 3: fatty liver necrosis with balloon-like degeneration, type 4: Mallory body or fiber Type 3 and type 4 with a high frequency of progression to cirrhosis or liver-related death are considered NASH. As for NASH, there are four types of Bruns depending on the degree of fibrosis (Stage 1: Central part of leaflet, Stage 2: Central part of leaflet to portal vein region, Stage 3: Cross-linking formation, Stage 4: Cirrhosis).

The correlation between NASH and NAFLD and obesity or metabolic syndrome is strong. In the obese group, 60-70% are NAFLD type 1, 20-25% are NAFLD type 3 or 4 (ie NASH), and 2-3% are cirrhosis (ie NASH Stage 4) . In addition, the frequency of dyslipidemia, hypertension, and hyperglycemia in NASH is 60%, 60%, and 30%, respectively, and the frequency of metabolic syndrome is as high as about 50%.

Fatty liver, which is the basis of NASH and NAFLD, is seen in 20-30% of all examinees, and has been on the rise in recent years, just like metabolic syndrome. Corresponding to the progress of liver symptoms from fatty liver to NAFLD and NASH, NAFLD is seen in 8% of the screening examinees, and the frequency of NASH is estimated to be 0.5-1% of adults. The prognosis of NASH is poor. In NASH, fibrosis has progressed to 25% in 5 years and 15% has progressed to cirrhosis. The survival rate of NASH is 67% in 5 years and 59% in 10 years.

Regarding the onset mechanism of NAFLD and NASH, “two-hit theory” is supported in which fat accumulation in hepatocytes occurs first, and then hepatocellular injury factors such as oxidative stress are added, leading to onset. . For treatment of NAFLD and NASH, exercise / dietary therapy for obesity improvement, and pharmacotherapy using insulin resistance improving drug, biguanite drug, ursodeoxycholic acid, antihyperlipidemic drug, antioxidant drug, etc. Although being studied, there are few studies on therapies using controls.

A definitive diagnosis of NAFLD and NASH requires liver histology by liver biopsy. However, liver biopsy has a high degree of invasiveness and is burdensome to the patient because it involves the patient's pain and further risks such as bleeding. Therefore, it is practically impossible to perform liver biopsy on 20-30% of all examinees who have fatty liver.

From such a current situation, first, a case with high possibility of NASH or NAFLD is determined by a simple method with less invasion instead of liver biopsy, and the determined case is subjected to NASH diagnosis by liver biopsy and treatment It is desirable from the viewpoint of physical burden on patients and medical economics.

To date, minimally invasive NAFLD and NASH discriminating methods include transaminase (ALT> AST), increased γGTP, increased AST / ALT ratio, fibrosis markers such as hyaluronic acid, decreased platelet count, and insulin resistance HOMA index, oxidative stress marker, adipocyte pokine such as adiponectin, and highly sensitive CRP have been reported (Non-patent Document 1 and Non-patent Document 2).

In addition, Fischer's ratio “(Leu + Val + Ile) / (Phe + Tyr)” proposed by Fischer, or the Fischer ratio used for clinical diagnosis for the same purpose as the Fischer ratio, is simply used as an index to use blood amino acid concentration for liver disease diagnosis. There is a BTR index “(Leu + Val + Ile) / Tyr” (Non-patent Document 3).

Also, Patent Literature 1, Patent Literature 2, and Patent Literature 3 relating to a method for associating an amino acid concentration with a biological state are disclosed as prior patents. Patent Document 1 discloses a method for diagnosing hepatitis using amino acids in blood and an index for the purpose of discriminating non-hepatitis from hepatitis C and hepatitis. Further, Patent Document 4 relating to an apparatus for evaluating the progression of a disease state of liver disease using an index formula consisting of a fractional expression with the amino acid concentration as a variable is disclosed. In addition, Patent Document 5 relating to a method for evaluating metabolic syndrome using amino acid concentration, Patent Document 6 relating to a method for evaluating visceral fat accumulation using amino acid concentration, and glucose tolerance abnormality using amino acid concentration. Patent Document 7 relating to a method for evaluating the state and Patent Document 8 relating to a method for evaluating the state of apparent obesity, hidden obesity, and obesity using amino acid concentrations are disclosed.

International Publication No. 2004/052191 International Publication No. 2006/098192 International Publication No. 2009/054351 International Publication No. 2006/129513 International Publication No. 2008/015929 International Publication No. 2009/001862 International Publication No. 2009/0535050 International Publication No. 2010/095682

However, there have been no reports on the correlation between fatty liver, NAFLD, and NASH and blood amino acid concentration, and on the application of blood amino acid concentration to the method for discriminating fatty liver, NAFLD, and NASH. Note that the discrimination methods reported in Non-Patent Literature 1 and Non-Patent Literature 2 do not have sufficient diagnostic performance, so it is difficult to apply the discrimination methods as established diagnostic methods. In addition, since the Fischer ratio and BTR index reported in Non-Patent Document 3 are used for diagnosis of hepatic encephalopathy in cirrhosis, sufficient accuracy can be obtained even if the Fischer ratio and BTR index are used for the diagnosis of NAFLD or NASH. Can't get. Further, even when the index formula groups disclosed in Patent Documents 1 to 8 are used for diagnosis of NAFLD or NASH, the diagnosis target is different, so that sufficient accuracy cannot be obtained.

In other words, a method for evaluating the state of fatty liver diseases such as fatty liver, NAFLD, and NASH using a plurality of amino acids as variables has not been developed so far, and there has been a problem that it has not been put into practical use. It was.

The present invention has been made in view of the above problems, and uses an amino acid concentration in blood to accurately evaluate the state of fatty liver disease. Liver disease evaluation device, fatty liver disease evaluation method, fatty liver disease evaluation program, fatty liver disease evaluation system, information communication terminal device, and fatty liver disease evaluation method It is an object of the present invention to provide a method for searching for a substance for preventing / ameliorating fatty liver disease, which can accurately search for a substance that can prevent or improve the state of fatty liver disease.

Amino acids are mainly metabolized in the liver, and the progression from fatty liver to NAFLD and NASH is considered to be strongly correlated with glucose metabolism, lipid metabolism, inflammatory response, and oxidative stress response. Therefore, if a blood amino acid that varies in response to changes in the histology of the liver in the state of NAFLD or NASH is identified, and if an index expression is found with the concentration of the identified blood amino acid as a variable, fatty liver, NAFLD , And NASH can be widely applied as a simple and effective discrimination method. As a result of intensive studies to solve the above-mentioned problems, the present inventors have identified amino acid variables useful for discriminating fatty liver, NAFLD, and NASH positive groups based on the amino acid concentration in blood. The present inventors have completed the present invention by finding a multivariate discriminant (function formula, index formula) for optimizing the discriminating ability between the two groups using the amino acid concentration as a variable.

That is, in order to solve the above-described problems and achieve the object, the method for evaluating fatty liver disease according to the present invention obtains amino acid concentration data relating to the concentration value of amino acids in blood collected from an evaluation object. And the fatty acid containing at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis) based on the amino acid concentration data obtained in the obtaining step. And a concentration value reference evaluation step for evaluating the state of the liver disease.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the concentration value reference evaluation step includes Gln, Glu included in the amino acid concentration data acquired in the acquisition step. , Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser. Based on the concentration value, the NASH state is evaluated for the evaluation object. It is characterized by evaluating.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the concentration value reference evaluation step includes Gln, Glu included in the amino acid concentration data acquired in the acquisition step. , Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser, whether or not the NASH or non-NASH based on the concentration value The method further includes a density value reference discrimination step for discriminating.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the concentration value reference evaluation step includes Gln, Glu included in the amino acid concentration data acquired in the acquisition step. , Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn. And evaluating the state of the NAFLD.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the concentration value reference evaluation step includes Gln, Glu included in the amino acid concentration data acquired in the acquisition step. , Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn. The method further includes a density value reference determining step of determining whether the NAFLD or non-NAFLD.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the concentration value reference evaluation step includes Thr, Ser included in the amino acid concentration data acquired in the acquisition step. , Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn. It is characterized by evaluating the state of.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the concentration value reference evaluation step includes Thr, Ser included in the amino acid concentration data acquired in the acquisition step. , Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn. Alternatively, the method further includes a concentration value reference determining step for determining whether or not the patient is non-fatty liver.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the concentration value reference evaluation step includes Gln, Glu included in the amino acid concentration data acquired in the acquisition step. , Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, ABA, the state of the NASH and the NAFLD is evaluated for the evaluation object based on the concentration value It is characterized by doing.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the concentration value reference evaluation step includes Gln, Glu included in the amino acid concentration data acquired in the acquisition step. , Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, ABA, based on the concentration value, the NASH or the non-NASH and the NAFLD It further includes a density value reference determining step for determining whether or not

The fatty liver disease evaluation method according to the present invention is the fatty liver disease evaluation method, wherein the concentration value reference evaluation step includes the amino acid concentration data acquired in the acquisition step, and the amino acid concentration. Based on a preset multivariate discriminant including a variable, a discriminant value calculating step for calculating a discriminant value that is a value of the multivariate discriminant, and based on the discriminant value calculated in the discriminant value calculating step, The evaluation object further includes: a discriminant value criterion evaluation step for evaluating the state of the fatty liver disease.

The method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the multivariate discriminant is a logistic regression equation, a fractional equation, a linear discriminant, a multiple regression equation, a support vector, It is one of an expression created by a machine, an expression created by the Mahalanobis distance method, an expression created by a canonical discriminant analysis, and an expression created by a decision tree.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the discriminant value calculating step includes Gln, Glu, and Gln included in the amino acid concentration data acquired in the acquiring step. The concentration value of at least one of Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser, and Gln, Glu, Pro, Gly, Ala, Leu, Ile , Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser, based on the multivariate discriminant including at least one of the variables, calculating the discriminant value, and the discriminant value criterion evaluation step Is based on the discriminant value calculated in the discriminant value calculating step, for each of the evaluation objects, the NASH Evaluating the state, characterized by.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the discriminant value criterion evaluating step is based on the discriminant value calculated in the discriminant value calculating step. The method further includes a discriminant value criterion discriminating step for discriminating whether the evaluation target is the NASH or the non-NASH.

The method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the multivariate discriminant includes Glu, Gln, Gly, Ala, Val, Tyr as the variable. It is a logistic regression equation.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the discriminant value calculating step includes Gln, Glu, and Gln included in the amino acid concentration data acquired in the acquiring step. Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn, the concentration value, and Gln, Glu, Pro, Gly , Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn based on the multivariate discriminant including at least one as the variable, A discriminant value is calculated, and the discriminant value reference evaluation step includes the discriminant value calculated in the discriminant value calculation step. Based on the value, per the evaluation object, evaluating the state of the NAFLD, characterized.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the discriminant value criterion evaluating step is based on the discriminant value calculated in the discriminant value calculating step. The method further includes a discriminant value criterion discriminating step for discriminating whether the evaluation object is the NAFLD or the non-NAFLD.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the multivariate discriminant includes Ser, Glu, Gly, Val, Tyr, and His as the variables. It is a logistic regression equation.

The method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the discriminant value calculating step includes Thr, Ser, and the like included in the amino acid concentration data acquired in the acquiring step. At least one concentration value of Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn, and Thr, Ser, Glu, Pro, Gly, Ala , Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn based on the multivariate discriminant including at least one of the variables, the discriminant value is calculated, and the discrimination is performed. In the value criterion evaluation step, the evaluation target is evaluated based on the discriminant value calculated in the discriminant value calculation step. , Evaluating the state of the fatty liver, characterized by.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the discriminant value criterion evaluating step is based on the discriminant value calculated in the discriminant value calculating step. The method further includes a discriminant value criterion discriminating step for discriminating whether the evaluation target is the fatty liver or non-fatty liver.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the multivariate discriminant includes Ser, Glu, Gly, Ala, Val, Tyr as the variable. It is a logistic regression equation.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the discriminant value calculating step includes Gln, Glu, and Gln included in the amino acid concentration data acquired in the acquiring step. Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, ABA, at least one of the concentration values, and Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn , Phe, Met, Ile, Pro, ABA, the discriminant value is calculated based on the multivariate discriminant including at least one of the variables as the variable, and the discriminant value criterion evaluation step includes: Based on the calculated discriminant value, the state of the NASH and the NAFLD is evaluated for the evaluation object. Rukoto, characterized by.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the discriminant value criterion evaluating step is based on the discriminant value calculated in the discriminant value calculating step. The method further includes a discriminant value criterion discriminating step for discriminating whether the evaluation target is the NASH or non-NASH and the NAFLD.

The method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the multivariate discriminant includes Asn, Gln, Gly, Ala, Cit, and Met as the variables. It is a logistic regression equation.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the discriminant value criterion evaluating step is based on the discriminant value calculated in the discriminant value calculating step. The method further includes a discriminant value criterion discriminating step for discriminating whether the evaluation object is non-NAFLD, NASH, or non-NASH and NAFLD.

Further, the method for evaluating fatty liver disease according to the present invention is the method for evaluating fatty liver disease, wherein the multivariate discriminant includes Ser, Glu, Gly, Val, Tyr, and His as the variables. A logistic regression equation and the logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as the variables.

The fatty liver disease evaluation apparatus according to the present invention includes a control unit and a storage unit, and includes, among evaluation targets, fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis). A fatty liver disease evaluation apparatus that evaluates a state of fatty liver disease including at least one, wherein the control means includes the previously obtained amino acid concentration data of the evaluation object relating to the amino acid concentration value, and the concentration of the amino acid. Based on the multivariate discriminant stored in the storage means including the variable, the discriminant value calculating unit that calculates the discriminant value that is the value of the multivariate discriminant, and the discriminant value calculated by the discriminant value calculating unit Based on the evaluation subject, the status of the fatty liver disease Further comprising a worthy discriminant value criterion-evaluating unit, characterized by.

The fatty liver disease evaluation apparatus according to the present invention is the fatty liver disease evaluation apparatus, wherein the control means is the fatty liver disease state relating to the amino acid concentration data and an index representing the state of the fatty liver disease. A multivariate discriminant creating unit that creates the multivariate discriminant stored in the storage unit based on fatty liver disease state information stored in the storage unit including index data; and the multivariate discriminant The creating means includes a candidate multivariate discriminant creating means for creating a candidate multivariate discriminant that is a candidate for the multivariate discriminant based on a predetermined formula creating method from the fatty liver disease state information; Based on the candidate multivariate discriminant verification means for verifying the candidate multivariate discriminant created by the variable discriminant creation means based on a predetermined verification method, and the verification results of the candidate multivariate discriminant verification means By selecting the variable of the candidate multivariate discriminant based on the variable selection method of the above (however, the variable of the candidate multivariate discriminant is selected based on the predetermined variable selection method without considering the verification result) And variable selection means for selecting a combination of the amino acid concentration data included in the fatty liver disease state information used when creating the candidate multivariate discriminant. Based on the verification results accumulated by repeatedly executing the variable discriminant creation means, the candidate multivariate discriminant verification means, and the variable selection means, the multivariate discriminant is selected from the plurality of candidate multivariate discriminants. The multivariate discriminant may be created by selecting the candidate multivariate discriminant to be adopted.

In addition, the method for evaluating fatty liver disease according to the present invention includes a fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH, which are executed in an information processing apparatus including a control unit and a storage unit. A fatty liver disease evaluation method for evaluating a state of fatty liver disease comprising at least one of (non-alcoholic steatohepatitis), the evaluation obtained in advance concerning the concentration value of amino acid, executed in the control means Based on the target amino acid concentration data and the multivariate discriminant stored in the storage means including the amino acid concentration as a variable, a discriminant value calculating step for calculating a discriminant value that is a value of the multivariate discriminant; and The discriminant value calculated in the discriminant value calculating step Based on, per the evaluation object, comprise a discriminant value criterion evaluating step of evaluating the state of the fatty liver disease, characterized by.

Moreover, the fatty liver disease evaluation program according to the present invention is performed on an information processing apparatus including a control unit and a storage unit. The evaluation target includes fatty liver, NAFLD (non-alcoholic fatty liver disease), and A fatty liver disease evaluation program for evaluating a state of fatty liver disease including at least one of NASH (non-alcoholic steatohepatitis), which is obtained in advance with respect to an amino acid concentration value to be executed by the control means A discriminant value calculating step for calculating a discriminant value which is a value of the multivariate discriminant based on the amino acid concentration data to be evaluated and the multivariate discriminant stored in the storage means including the amino acid concentration as a variable; , The discriminant value calculation step Based on the discriminant value calculated at flop, per the evaluation object, comprise a discriminant value criterion evaluating step of evaluating the state of the fatty liver disease, characterized by.

Further, a recording medium according to the present invention is a computer-readable recording medium, and is characterized by recording the above-mentioned fatty liver disease evaluation program.

Moreover, the fatty liver disease evaluation system according to the present invention comprises a control means and a storage means. The evaluation target includes fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis). A fatty liver disease evaluation apparatus that evaluates the state of fatty liver disease including at least one, and an information communication terminal device that includes a control means and provides the amino acid concentration data of the evaluation object related to the amino acid concentration value, The fatty liver disease evaluation system configured to be communicably connected via the information communication terminal device, wherein the control means of the information communication terminal device sends the amino acid concentration data to be evaluated to the fatty liver disease evaluation device Amino acid concentration data transmission means to be transmitted and the previous Evaluation result receiving means for receiving the evaluation result of the evaluation object related to the state evaluation of the fatty liver disease transmitted from the fatty liver disease evaluation apparatus, and the control means of the fatty liver disease evaluation apparatus comprises: Amino acid concentration data receiving means for receiving the amino acid concentration data transmitted from the information communication terminal device, the amino acid concentration data received by the amino acid concentration data receiving means, and the storage means including the amino acid concentration as variables Based on the determined multivariate discriminant, a discriminant value calculating unit that calculates a discriminant value that is a value of the multivariate discriminant, and based on the discriminant value calculated by the discriminant value calculating unit, Discriminant value reference evaluation means for evaluating the state of fatty liver disease, and the evaluation result of the evaluation object in the discriminant value reference evaluation means as the information communication Further comprising an evaluation result transmitting means for transmitting to the end device, and characterized.

In addition, the information communication terminal device according to the present invention is a state of fatty liver disease including at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis) per evaluation object. An information communication terminal device that provides control means connected to a fatty liver disease evaluation device for assessing a network via a network, and provides the amino acid concentration data of the evaluation object related to the amino acid concentration value, The control means relates to amino acid concentration data transmitting means for transmitting the amino acid concentration data to be evaluated to the fatty liver disease evaluating apparatus, and state evaluation of the fatty liver disease transmitted from the fatty liver disease evaluating apparatus. Receive the evaluation result of the evaluation target Evaluation result receiving means, wherein the evaluation result indicates that the fatty liver disease evaluation device receives the amino acid concentration data transmitted from the information communication terminal device, and the received amino acid concentration data, and the amino acid Based on the multivariate discriminant stored in the apparatus for assessing fatty liver disease including the concentration of the liver as a variable, a discriminant value that is the value of the multivariate discriminant is calculated, and the evaluation is performed based on the calculated discriminant value. It is a result of evaluating the state of the fatty liver disease per subject.

The fatty liver disease evaluation apparatus according to the present invention includes a control unit and a storage unit that are communicably connected to an information communication terminal device that provides amino acid concentration data to be evaluated regarding amino acid concentration values via a network. The evaluation subject is a fatty liver disease evaluation for evaluating the status of fatty liver disease including at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis) An amino acid concentration data receiving means for receiving the amino acid concentration data transmitted from the information communication terminal device, the amino acid concentration data received by the amino acid concentration data receiving means, and the amino acid Concentration of Based on the multivariate discriminant stored in the storage unit included as a variable, based on the discriminant value calculated by the discriminant value calculated by the discriminant value calculator and the discriminant value calculated by the discriminant value calculator A discriminant value criterion-evaluating unit that evaluates the state of fatty liver disease for the evaluation object, and an evaluation result transmission that transmits the evaluation result of the evaluation object in the discriminant value criterion-evaluating unit to the information communication terminal device And means.

The method for searching for a substance for preventing / ameliorating fatty liver disease according to the present invention is an amino acid related to the concentration value of amino acids in blood collected from an evaluation subject to which a desired substance group consisting of one or more substances is administered. Based on the acquisition step of acquiring concentration data and the amino acid concentration data acquired in the acquisition step, for the evaluation target, fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis) Based on the evaluation result in the concentration value reference evaluation step for evaluating the state of fatty liver disease including at least one of them, and the concentration value reference evaluation step, the desired substance group has the fatty liver disease Prevent or treat the condition of fatty liver disease Characterized in that it comprises a a judgment step of judging whether one which good, the.

According to the present invention, amino acid concentration data relating to the concentration value of amino acids in blood collected from an evaluation object is obtained, and based on the obtained amino acid concentration data, fatty acid liver, NAFLD (non-alcoholic fatity river) is obtained for the evaluation object. disease), and the status of fatty liver disease including at least one of NASH (non-alcoholic steatohepatitis). Thereby, there exists an effect that the state of fatty liver disease can be accurately evaluated using the concentration of amino acids in blood.

Moreover, according to the present invention, at least one of Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser included in the amino acid concentration data. Based on the density value, the state of NASH is evaluated for each evaluation target. This produces an effect that the NASH state can be accurately evaluated using the amino acid concentration related to the NASH state among the amino acid concentrations in the blood.

Moreover, according to the present invention, at least one of Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser included in the amino acid concentration data. Based on the density value, it is determined whether or not it is NASH or non-NASH. As a result, the amino acid concentration useful for the two-group discrimination of NASH and non-NASH among the amino acid concentrations in the blood is utilized, and this has the effect that the two-group discrimination can be performed with high accuracy.

Further, according to the present invention, Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, which are included in the amino acid concentration data. Based on the concentration value of at least one of Asn, the state of NAFLD is evaluated for each evaluation target. Accordingly, the NAFLD state can be accurately evaluated using the amino acid concentration related to the NAFLD state among the amino acid concentrations in the blood.

Further, according to the present invention, Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, which are included in the amino acid concentration data. Based on the concentration value of at least one of Asn, it is determined whether the evaluation target is NAFLD or non-NAFLD. As a result, the amino acid concentration useful for the 2-group discrimination between NAFLD and non-NAFLD among the amino acid concentrations in the blood is utilized, and this has the effect that the 2-group discrimination can be accurately performed.

According to the present invention, at least one of Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn included in the amino acid concentration data. Based on one concentration value, the state of fatty liver is evaluated for each evaluation object. This produces an effect that the fatty liver state can be accurately evaluated using the amino acid concentration related to the fatty liver state among the amino acid concentrations in the blood.

According to the present invention, at least one of Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn included in the amino acid concentration data. Based on one concentration value, it is discriminated whether the subject of evaluation is fatty liver or non-fatty liver. As a result, the amino acid concentration useful for the 2-group discrimination between fatty liver and non-fatty liver among the amino acid concentrations in the blood can be used, and this 2-group discrimination can be accurately performed.

Further, according to the present invention, based on at least one concentration value among Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA contained in amino acid concentration data. Then, the state of NASH and NAFLD is evaluated for each evaluation object. This produces an effect that the state of NASH and NAFLD can be accurately evaluated using the concentration of amino acids related to the state of NASH and NAFLD among the concentrations of amino acids in blood.

Further, according to the present invention, based on at least one concentration value among Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA contained in amino acid concentration data. Thus, it is determined whether the evaluation target is NASH or non-NASH and NAFLD. As a result, the amino acid concentration useful for the 2-group discrimination between NASH and simple fatty liver among the amino acid concentrations in the blood can be used, and this 2-group discrimination can be accurately performed.

Further, according to the present invention, based on the amino acid concentration data and a preset multivariate discriminant including the amino acid concentration as a variable, a discriminant value that is the value of the multivariate discriminant is calculated, and the calculated discriminant value Based on the above, the status of fatty liver disease is evaluated for each evaluation subject. Thereby, the discriminant value obtained by the multivariate discriminant including the amino acid concentration as a variable can be used to produce an effect that the state of fatty liver disease can be accurately evaluated.

Further, according to the present invention, the multivariate discriminant is a logistic regression equation, a fractional equation, a linear discriminant equation, a multiple regression equation, an equation created by a support vector machine, an equation created by the Mahalanobis distance method, a canonical discriminant. One of an expression created by analysis and an expression created by a decision tree. Accordingly, the discriminant value obtained by the multivariate discriminant including the amino acid concentration as a variable can be used to produce an effect that the state of fatty liver disease can be more accurately evaluated.

Moreover, according to the present invention, at least one of Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser included in the amino acid concentration data. Based on a concentration value and a multivariate discriminant including at least one of Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser as a variable. Then, the discriminant value is calculated, and the state of NASH is evaluated for each evaluation object based on the calculated discriminant value. Thus, the NASH state can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the NASH state.

Further, according to the present invention, based on the discriminant value, it is discriminated whether the evaluation target is NASH or non-NASH. Thus, the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between NASH and non-NASH is used, and this has the effect that the two-group discrimination can be performed with high accuracy.

Further, according to the present invention, the multivariate discriminant is a logistic regression equation including Glu, Gln, Gly, Ala, Val, and Tyr as variables. Accordingly, the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination of NASH and non-NASH is used, and this has the effect that the two-group discrimination can be performed with higher accuracy.

Further, according to the present invention, Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, which are included in the amino acid concentration data. At least one concentration value of Asn, and at least of Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn A discriminant value is calculated based on a multivariate discriminant including one as a variable, and the state of NAFLD is evaluated for each evaluation object based on the calculated discriminant value. Thereby, the NAFLD state can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the NAFLD state.

Further, according to the present invention, it is determined whether the evaluation target is NAFLD or non-NAFLD based on the determination value. Accordingly, the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between NAFLD and non-NAFLD can be used to achieve the effect that the two-group discrimination can be performed with high accuracy.

Further, according to the present invention, the multivariate discriminant is a logistic regression equation including Ser, Glu, Gly, Val, Tyr, and His as variables. Thus, the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination of NAFLD and non-NAFLD is used, and this has the effect that the two-group discrimination can be performed more accurately.

According to the present invention, at least one of Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn included in the amino acid concentration data. Multivariate including one concentration value and at least one of Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn as a variable A discriminant value is calculated based on the discriminant, and the state of fatty liver is evaluated for each evaluation object based on the calculated discriminant value. This produces an effect that the state of fatty liver can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the state of fatty liver.

Further, according to the present invention, it is determined whether the evaluation target is fatty liver or non-fatty liver based on the discriminant value. Thereby, the discriminant value obtained by the multivariate discriminant useful for discriminating between the two groups of fatty liver and non-fatty liver is used, and the effect that the two-group discrimination can be performed with high accuracy is achieved.

Further, according to the present invention, the multivariate discriminant is a logistic regression equation including Ser, Glu, Gly, Ala, Val, and Tyr as variables. Accordingly, the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between fatty liver and non-fatty liver is used, and this has the effect that the two-group discrimination can be performed with higher accuracy.

Further, according to the present invention, at least one concentration value of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, ABA included in the amino acid concentration data, and A discriminant value is calculated based on a multivariate discriminant including at least one of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA as a variable, Based on the calculated discriminant value, the state of NASH and NAFLD is evaluated for each evaluation target. Thus, the NASH and NAFLD states can be accurately evaluated using the discriminant values obtained by the multivariate discriminant having a significant correlation with the NASH and NAFLD states.

Further, according to the present invention, based on the discriminant value, it is discriminated whether the evaluation target is NASH, non-NASH and NAFLD. Thus, the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between NASH and simple fatty liver can be used to achieve the effect that the two-group discrimination can be performed with high accuracy.

Further, according to the present invention, the multivariate discriminant is a logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as variables. As a result, the two-group discrimination can be performed more accurately by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between NASH and simple fatty liver.

Further, according to the present invention, based on the discriminant value, it is discriminated whether the evaluation target is non-NAFLD, NASH, non-NASH and NAFLD. Thus, the three-group discrimination can be performed with high accuracy by using the discriminant value obtained by the multivariate discriminant useful for the three-group discrimination of non-NAFLD, NASH, and simple fatty liver.

According to the present invention, the multivariate discriminant includes logistic regression equations including Ser, Glu, Gly, Val, Tyr, and His as variables, and logistics including Asn, Gln, Gly, Ala, Cit, and Met as variables. It is a regression equation. As a result, the three-group discrimination can be performed more accurately by using the discriminant value obtained by the multivariate discriminant particularly useful for the three-group discrimination of non-NAFLD, NASH, and simple fatty liver. .

According to the present invention, the storage means is based on the fatty liver disease state information stored in the storage means including the amino acid concentration data and the fatty liver disease state index data relating to the index representing the state of fatty liver disease. A multivariate discriminant to be stored may be created. Specifically, (1) a candidate multivariate discriminant is created from fatty liver disease state information based on a predetermined formula creation method, and (2) the created candidate multivariate discriminant is based on a predetermined verification method. (3) By selecting a variable of a candidate multivariate discriminant from the verification result based on a predetermined variable selection method (however, the candidate is based on the predetermined variable selection method without considering the verification result) A variable of the multivariate discriminant may be selected.), A combination of amino acid concentration data included in the fatty liver disease state information used when creating the candidate multivariate discriminant is selected, and (4) (1) By selecting a candidate multivariate discriminant to be adopted as a multivariate discriminant from a plurality of candidate multivariate discriminants based on the verification results accumulated by repeatedly executing (2) and (3), A variable discriminant may be created. Thereby, there exists an effect that the multivariate discriminant optimal for the state evaluation of fatty liver disease can be created.

According to the present invention, since the fatty liver disease evaluation program recorded on the recording medium is read and executed by the computer, the computer executes the fatty liver disease evaluation program. There is an effect that can be obtained.

Further, according to the present invention, amino acid concentration data relating to amino acid concentration values collected from an evaluation subject to which a desired substance group consisting of one or a plurality of substances is administered is obtained, and the obtained amino acid concentration data is obtained. Based on the evaluation results, the evaluation target was evaluated for the status of fatty liver disease including at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis). Since it is determined whether the desired substance group is for preventing fatty liver disease or improving the state of fatty liver disease, the concentration of amino acids in blood is used to determine Using a method for assessing fatty liver disease that can accurately assess the condition, Substances which improve the condition or fatty liver disease is prevented 肪性 liver disease is an effect that it is possible to search accurately. In addition, according to the present invention, by using information on typical amino acid concentration fluctuation patterns in fatty liver disease and multivariate discriminants corresponding to fatty liver disease, the state of fatty liver disease is partially It is possible to select an existing animal model that reflects and an effective drug at an early stage in clinical practice.

In the present invention, when assessing the state of fatty liver disease, in addition to the concentration of amino acids, other biological information (for example, biological metabolites such as sugars, lipids, proteins, peptides, minerals, hormones,・ Blood pressure value, gender, age, liver disease index, eating habits, drinking habits, exercise habits, obesity level, disease history, etc. may be further used. In addition, the present invention, when assessing the state of fatty liver disease, as a variable in the multivariate discriminant, in addition to the amino acid concentration, other biological information (for example, sugars, lipids, proteins, peptides, minerals, hormones, etc.) Biological metabolites and biological indices such as blood glucose level, blood pressure level, gender, age, liver disease index, dietary habits, drinking habits, exercise habits, obesity level, disease history, etc. may be further used.

FIG. 1 is a principle configuration diagram showing the basic principle of the present invention. FIG. 2 is a flowchart showing an example of the method for evaluating fatty liver disease according to the first embodiment. FIG. 3 is a principle configuration diagram showing the basic principle of the present invention. FIG. 4 is a diagram illustrating an example of the overall configuration of the present system. FIG. 5 is a diagram showing another example of the overall configuration of the present system. FIG. 6 is a block diagram showing an example of the configuration of the fatty liver disease evaluation apparatus 100 of the present system. FIG. 7 is a diagram illustrating an example of information stored in the user information file 106a. FIG. 8 is a diagram showing an example of information stored in the amino acid concentration data file 106b. FIG. 9 is a diagram illustrating an example of information stored in the fatty liver disease state information file 106c. FIG. 10 is a diagram illustrating an example of information stored in the designated fatty liver disease state information file 106d. FIG. 11 is a diagram illustrating an example of information stored in the candidate multivariate discriminant file 106e1. FIG. 12 is a diagram illustrating an example of information stored in the verification result file 106e2. FIG. 13 is a diagram illustrating an example of information stored in the selected fatty liver disease state information file 106e3. FIG. 14 is a diagram illustrating an example of information stored in the multivariate discriminant file 106e4. FIG. 15 is a diagram illustrating an example of information stored in the discrimination value file 106f. FIG. 16 is a diagram illustrating an example of information stored in the evaluation result file 106g. FIG. 17 is a block diagram showing a configuration of the multivariate discriminant-preparing part 102h. FIG. 18 is a block diagram illustrating a configuration of the discriminant value criterion-evaluating unit 102j. FIG. 19 is a block diagram illustrating an example of the configuration of the client apparatus 200 of the present system. FIG. 20 is a block diagram showing an example of the configuration of the database apparatus 400 of this system. FIG. 21 is a flowchart showing an example of fatty liver disease evaluation service processing performed in the present system. FIG. 22 is a flowchart showing an example of multivariate discriminant creation processing performed by the fatty liver disease evaluation apparatus 100 of the present system. FIG. 23 is a principle configuration diagram showing the basic principle of the present invention. FIG. 24 is a flowchart illustrating an example of a method for searching for a substance for preventing / ameliorating fatty liver disease according to the third embodiment. FIG. 25 is a diagram showing a list of logistic regression equations having good discrimination ability for discrimination between fatty liver positive and fatty liver negative. FIG. 26 is a diagram showing a list of logistic regression equations having good discrimination ability for discrimination between fatty liver positive and fatty liver negative. FIG. 27 is a diagram showing a list of fractional expressions having good discrimination ability for discrimination between fatty liver positive and fatty liver negative. FIG. 28 is a diagram showing a list of fractional expressions having good discrimination ability for discrimination between fatty liver positive and fatty liver negative. FIG. 29 is a diagram showing a list of logistic regression equations having good discrimination ability for discrimination between NAFLD positive and NAFLD negative. FIG. 30 is a diagram showing a list of logistic regression equations having good discrimination ability for discrimination between NAFLD positive and NAFLD negative. FIG. 31 is a diagram showing a list of fractional expressions having good discrimination ability for discrimination between NAFLD positive and NAFLD negative. FIG. 32 is a diagram showing a list of fractional expressions having good discrimination ability for discrimination between NAFLD positive and NAFLD negative. FIG. 33 is a diagram showing a list of logistic regression equations having good discrimination ability for discrimination between NASH positive and NASH negative. FIG. 34 is a diagram showing a list of logistic regression equations having good discrimination ability for discrimination between NASH positive and NASH negative. FIG. 35 is a diagram showing a list of fractional expressions having good discrimination ability for discrimination between NASH positive and NASH negative. FIG. 36 is a diagram showing a list of fractional expressions having good discrimination ability for discrimination between NASH positive and NASH negative. FIG. 37 is a diagram showing a list of logistic regression equations having good discrimination ability for discrimination between NASH positive and simple fatty liver. FIG. 38 is a diagram showing a list of logistic regression equations having good discrimination ability for discrimination between NASH positive and simple fatty liver. FIG. 39 is a diagram showing a list of fractional expressions having good discrimination ability for discrimination between NASH positive and simple fatty liver. FIG. 40 is a diagram showing a list of fractional expressions having good discrimination ability for discrimination between NASH positive and simple fatty liver. FIG. 41 is a diagram showing discrimination results for discrimination between NAFLD negative, simple fatty liver, and NASH positive. FIG. 42 is a diagram showing details of a discrimination result for discrimination between NAFLD negative, simple fatty liver, and NASH positive.

Hereinafter, an embodiment (first embodiment) of an evaluation method for fatty liver disease according to the present invention, an apparatus for evaluating fatty liver disease, an evaluation method for fatty liver disease, an evaluation program for fatty liver disease, Embodiment of Recording Medium, Fatty Liver Disease Evaluation System, and Information Communication Terminal Device (Second Embodiment), and Embodiment of Method for Searching for Substance for Preventing / Improving Fatty Liver Disease According to the Present Invention (Third Embodiment) Embodiment) will be described in detail with reference to the drawings. In addition, this invention is not limited by this Embodiment.

[First Embodiment]
[1-1. Outline of the present invention]
Here, the outline | summary of the evaluation method of the fatty liver disease concerning this invention is demonstrated with reference to FIG. FIG. 1 is a principle configuration diagram showing the basic principle of the present invention.

First, amino acid concentration data relating to the concentration value of amino acids in blood (eg, including plasma, serum, etc.) collected from an evaluation target (eg, an individual such as an animal or a human) is acquired (step S11). In step S11, for example, amino acid concentration data measured by a company or the like that performs amino acid concentration measurement may be acquired. Further, for example, the following (A) or (B) may be obtained from blood collected from an evaluation target. Amino acid concentration data may be obtained by measuring amino acid concentration data by a measurement method. Here, the unit of amino acid concentration may be obtained by, for example, molar concentration, weight concentration, or by adding / subtracting / subtracting an arbitrary constant to / from these concentrations.
(A) Plasma was separated from blood by centrifuging the collected blood sample. All plasma samples were stored frozen at −80 ° C. until the measurement of amino acid concentration. For amino acid concentration measurement, acetonitrile was added to remove protein, followed by precolumn derivatization using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and liquid chromatography mass spectrometry The amino acid concentration was analyzed by a total (LC-MS) (see International Publication No. 2003/069328 and International Publication No. 2005/116629).
(B) Plasma was separated from blood by centrifuging the collected blood sample. All plasma samples were stored frozen at −80 ° C. until the measurement of amino acid concentration. When measuring the amino acid concentration, sulfosalicylic acid was added to remove the protein, and then the amino acid concentration was analyzed by an amino acid analyzer based on the post-column derivatization method using a ninhydrin reagent.

Next, based on the amino acid concentration data obtained in step S11, the fatty liver containing at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis) is evaluated based on the amino acid concentration data obtained in step S11. The state of the disease is evaluated (Step S12).

As described above, according to the present invention, amino acid concentration data relating to the concentration value of amino acids in blood collected from an evaluation object is obtained, and based on the obtained amino acid concentration data of the evaluation object, fatty liver, NAFLD, and Assess the status of fatty liver disease comprising at least one of NASH. Thereby, the state of fatty liver disease can be accurately evaluated using the concentration of amino acids in blood.

Here, before executing step S12, data such as missing values and outliers may be removed from the amino acid concentration data acquired in step S11. Thereby, the state of fatty liver disease can be more accurately evaluated.

In step S12, among the Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser included in the amino acid concentration data acquired in step S11. The state of NASH may be evaluated for the evaluation target based on at least one concentration value. Thereby, the state of NASH can be accurately evaluated using the concentration of amino acids related to the state of NASH among the concentrations of amino acids in blood. Specifically, at least one concentration value of Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser included in the amino acid concentration data. Based on this, it may be determined whether it is NASH or non-NASH. Thus, the amino acid concentration useful for the two-group discrimination of NASH and non-NASH among the amino acid concentrations in the blood can be used to accurately perform the two-group discrimination.

In step S12, Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser included in the amino acid concentration data acquired in step S11. , Thr, Asn, the state of NAFLD may be evaluated for each evaluation object based on at least one concentration value. Thereby, the state of NAFLD can be accurately evaluated using the concentration of amino acids related to the state of NAFLD among the concentrations of amino acids in blood. Specifically, among Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn included in the amino acid concentration data. Based on at least one concentration value, it may be determined whether the evaluation target is NAFLD or non-NAFLD. Thus, the amino acid concentration useful for the 2-group discrimination between NAFLD and non-NAFLD among the amino acid concentrations in the blood can be used to accurately perform the 2-group discrimination.

In step S12, Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn included in the amino acid concentration data acquired in step S11. The state of fatty liver may be evaluated for each evaluation object based on at least one concentration value. Thereby, the state of fatty liver can be accurately evaluated using the concentration of amino acids related to the state of fatty liver among the concentrations of amino acids in blood. Specifically, at least one concentration of Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn included in the amino acid concentration data. Based on the value, it may be determined whether or not the subject is a fatty liver or non-fatty liver. Thus, the amino acid concentration useful for the 2-group discrimination between fatty liver and non-fatty liver among the amino acid concentrations in the blood can be used to accurately perform the 2-group discrimination.

In step S12, at least one concentration of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA included in the amino acid concentration data acquired in step S11. Based on the value, the state of NASH and NAFLD may be evaluated for each evaluation target. Thereby, the state of NASH and NAFLD can be accurately evaluated using the concentration of amino acids related to the state of NASH and NAFLD among the concentrations of amino acids in blood. Specifically, based on the concentration value of at least one of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA included in the amino acid concentration data. Whether the subject is NASH or non-NASH and NAFLD may be determined. This makes it possible to accurately perform this 2-group discrimination by using the amino acid concentrations useful for 2-group discrimination between NASH and simple fatty liver among the amino acid concentrations in the blood.

In step S12, based on the amino acid concentration data acquired in step S11 and a preset multivariate discriminant including the amino acid concentration as a variable, a discriminant value that is the value of the multivariate discriminant is calculated and calculated. Based on the discriminated value, the state of fatty liver disease may be evaluated for each evaluation target. Thereby, the state of fatty liver disease can be accurately evaluated using the discriminant value obtained by the multivariate discriminant including the amino acid concentration as a variable.

Multivariate discriminants are logistic regression formula, fractional formula, linear discriminant formula, multiple regression formula, formula created by support vector machine, formula created by Mahalanobis distance method, formula created by canonical discriminant analysis. Any one of the expressions created by the decision tree may be used. Thereby, the state of fatty liver disease can be more accurately evaluated using the discriminant value obtained by the multivariate discriminant including the amino acid concentration as a variable.

In step S12, among the Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser included in the amino acid concentration data acquired in step S11. Multivariate discrimination including at least one concentration value and at least one of Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser A discriminant value may be calculated based on the formula, and the state of NASH may be evaluated for each evaluation object based on the calculated discriminant value. Thereby, the NASH state can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the NASH state. Specifically, it may be determined whether the evaluation target is NASH or non-NASH based on the determination value. This makes it possible to accurately perform the two-group discrimination using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between NASH and non-NASH. The multivariate discriminant may be a logistic regression equation including Glu, Gln, Gly, Ala, Val, Tyr as variables. Thereby, this two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination of NASH and non-NASH.

In step S12, Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser included in the amino acid concentration data acquired in step S11. , Thr, Asn, and Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn The discriminant value may be calculated based on a multivariate discriminant including at least one of them as a variable, and the state of NAFLD may be evaluated for each evaluation object based on the calculated discriminant value. Thereby, the NAFLD state can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the NAFLD state. Specifically, it may be determined whether the evaluation target is NAFLD or non-NAFLD based on the determination value. This makes it possible to accurately perform the two-group discrimination by using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between NAFLD and non-NAFLD. Note that the multivariate discriminant may be a logistic regression equation including Ser, Glu, Gly, Val, Tyr, and His as variables. This makes it possible to perform the two-group discrimination with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination of NAFLD and non-NAFLD.

In step S12, Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn included in the amino acid concentration data acquired in step S11. And at least one of Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn A discriminant value may be calculated based on the multivariate discriminant included, and the state of fatty liver may be evaluated for each evaluation object based on the calculated discriminant value. Thereby, the state of fatty liver can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the state of fatty liver. Specifically, based on the discriminant value, it may be discriminated whether the evaluation target is fatty liver or non-fatty liver. This makes it possible to accurately perform the two-group discrimination using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between fatty liver and non-fatty liver. The multivariate discriminant may be a logistic regression equation including Ser, Glu, Gly, Ala, Val, and Tyr as variables. Thus, the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between fatty liver and non-fatty liver.

In step S12, at least one concentration of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA included in the amino acid concentration data acquired in step S11. The discriminant value is based on a multivariate discriminant including at least one of a value and Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA as a variable. The state of NASH and NAFLD may be evaluated for each evaluation object based on the calculated discriminant value. Thereby, the state of NASH and NAFLD can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the state of NASH and NAFLD. Specifically, it may be determined whether the evaluation target is NASH or “non-NASH and NAFLD” (simple fatty liver) based on the determination value. This makes it possible to accurately perform this two-group discrimination using a discriminant value obtained by a multivariate discriminant useful for two-group discrimination between NASH and simple fatty liver. Note that the multivariate discriminant may be a logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as variables. Thereby, this two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between NASH and simple fatty liver. Specifically, it may be determined whether the evaluation target is non-NAFLD, NASH, or “non-NASH and NAFLD” based on the determination value. This makes it possible to accurately perform this three-group discrimination by using the discriminant value obtained by the multivariate discriminant useful for the three-group discrimination of non-NAFLD, NASH, and simple fatty liver. The multivariate discriminant may be a logistic regression equation including Ser, Glu, Gly, Val, Tyr, and His as variables, and a logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as variables. Thereby, this three-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the three-group discrimination of non-NAFLD, NASH, and simple fatty liver.

Here, each multivariate discriminant described above is described in the method described in International Publication No. 2004/052191 which is an international application by the present applicant or International Publication No. 2006/098192 which is an international application by the present applicant. You may produce by the method (The multivariate discriminant creation process as described in 2nd Embodiment mentioned later). If the multivariate discriminant obtained by these methods is used, the multivariate discriminant is preferably used for the evaluation of the state of fatty liver disease regardless of the unit of amino acid concentration in the amino acid concentration data as input data. be able to.

The multivariate discriminant generally means the format of formulas used in multivariate analysis. For example, fractional formulas, multiple regression formulas, multiple logistic regression formulas, linear discriminant functions, Mahalanobis distances, canonical discriminant functions, support vectors Includes machines, decision trees, etc. Also included are expressions as indicated by the sum of different forms of multivariate discriminants. In the multiple regression equation, multiple logistic regression equation, and canonical discriminant function, a coefficient and a constant term are added to each variable. In this case, the coefficient and the constant term are preferably real numbers, more preferably data. Values belonging to the range of 99% confidence intervals of the coefficients and constant terms obtained from the data, more preferably belonging to the range of 95% confidence intervals of the coefficients and constant terms obtained from the data Any value can be used. Further, the value of each coefficient and its confidence interval may be obtained by multiplying it by a real number, and the value of the constant term and its confidence interval may be obtained by adding / subtracting / multiplying / dividing an arbitrary real constant thereto. When using display formulas such as logistic regression, linear discriminant, multiple regression analysis as indicators, linear transformation (addition of constants, multiple of constants) or monotonically increasing (decreasing) transformations of display formulas (such as logit transformation) have discriminative performance. The display formulas include them because they are equivalent, not changed.

The fractional expression means that the numerator of the fractional expression is represented by the sum of amino acids A, B, C,... And / or the denominator of the fractional expression is the sum of amino acids a, b, c,. It is represented by In addition, the fractional expression includes a sum of fractional expressions α, β, γ,. The fractional expression also includes a divided fractional expression. An appropriate coefficient may be added to each amino acid used in the numerator and denominator. In addition, amino acids used in the numerator and denominator may overlap. Moreover, an appropriate coefficient may be attached to each fractional expression. The value of the coefficient of each variable and the value of the constant term may be real numbers. In the fractional expression, the combination of the numerator variable and the denominator variable is generally reversed in the sign of the correlation with the target variable, but since the correlation is maintained, it can be considered equivalent in discriminability. Combinations of swapping numerator and denominator variables are also included.

And when this invention evaluates the state of fatty liver disease, in addition to the concentration of amino acids, other biological information (for example, biological metabolites such as sugars, lipids, proteins, peptides, minerals, hormones,・ Blood pressure value, gender, age, liver disease index, dietary habits, drinking habits, exercise habits, obesity level, disease history, etc. may be further used. In addition, the present invention, when assessing the state of fatty liver disease, as a variable in the multivariate discriminant, in addition to the amino acid concentration, other biological information (for example, sugars, lipids, proteins, peptides, minerals, hormones, etc.) Biological metabolites and biological indices such as blood glucose level, blood pressure level, gender, age, liver disease index, dietary habits, drinking habits, exercise habits, obesity level, disease history, etc. may be further used.

[1-2. Method for Evaluating Fatty Liver Disease According to First Embodiment]
Here, the method for evaluating fatty liver disease according to the first embodiment will be described with reference to FIG. FIG. 2 is a flowchart showing an example of the method for evaluating fatty liver disease according to the first embodiment.

First, amino acid concentration data relating to the concentration value of amino acids in blood collected from individuals such as animals and humans is acquired (step SA11). In step SA11, for example, amino acid concentration data measured by a company or the like that performs amino acid concentration measurement may be acquired, and measurement such as (A) or (B) described above is performed from blood collected from an evaluation target. Amino acid concentration data may be obtained by measuring amino acid concentration data by a method.

Next, data such as missing values and outliers are removed from the amino acid concentration data of the individual obtained in step SA11 (step SA12).

Next, based on the amino acid concentration data of individuals from which data such as missing values and outliers have been removed in step SA12, the following is shown for each individual: ~ 15. Any one of these determinations is executed (step SA13).

11. Discrimination between NASH and non-NASH (i) At least one of Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser included in amino acid concentration data Determine whether each individual is NASH or non-NASH by comparing one concentration value with a preset threshold (cutoff value), or (ii) Gln, included in the amino acid concentration data Concentration value of at least one of Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser, and Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser On the basis of a multivariate discriminant including at least one of the variables, a discriminant value is calculated, and the calculated discriminant value is compared with a preset threshold value (cutoff value). It is determined whether or not it is NASH.

12 Discrimination between NAFLD and non-NAFLD (i) Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr included in amino acid concentration data , Asn to determine whether each individual is NAFLD or non-NAFLD by comparing at least one concentration value with a preset threshold (cutoff value), or (ii) amino acid concentration At least one concentration value of Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn included in the data, and Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, T A discriminant value is calculated based on a multivariate discriminant including at least one of yr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn as a variable, and the calculated discriminant value is set in advance. It is determined whether each individual is NAFLD or non-NAFLD by comparing with the threshold value (cutoff value).

13. Discrimination between fatty liver and non-fatty liver (i) Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn included in amino acid concentration data By comparing at least one concentration value and a preset threshold value (cut-off value), it is determined whether the individual has fatty liver or non-fatty liver, or (ii) amino acid concentration Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn included in the data, and Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, A By calculating a discriminant value based on a multivariate discriminant including at least one of sn and Orn as a variable, and comparing the calculated discriminant value with a preset threshold value (cutoff value), Determine whether it is fatty liver or non-fatty liver.

14 Discrimination between NASH and simple fatty liver (i) Concentration of at least one of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, ABA included in amino acid concentration data By comparing the value with a preset threshold value (cutoff value), it is determined whether the individual is NASH or simple fatty liver (non-NASH and NAFLD), or (ii) amino acid concentration Concentration value of at least one of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, ABA included in the data, and Gln, Glu, Gly, Ala, Cit, Atn one of Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, ABA Is calculated on the basis of a multivariate discriminant that includes a variable, and the calculated discriminant value is compared with a preset threshold value (cut-off value), whereby NASH or simple fatty liver ( It is determined whether it is non-NASH and NAFLD.

15. Discrimination between NASH, simple fatty liver and non-NAFLD Concentration of at least one of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro and ABA contained in amino acid concentration data The discriminant value is based on a multivariate discriminant including at least one of a value and Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA as a variable. By calculating and comparing the calculated discriminant value with a preset threshold value (cut-off value), it is determined whether or not each individual is non-NAFLD, NASH, or simple fatty liver (non-NASH and NAFLD). Determine.

[1-3. Summary of First Embodiment and Other Embodiments]
As described above in detail, according to the method for evaluating fatty liver disease according to the first embodiment, (i) amino acid concentration data in blood collected from an individual is acquired, and (ii) the acquired individual The data such as missing values and outliers are removed from the amino acid concentration data, and (iii) the above-mentioned 11. above for each individual based on the amino acid concentration data of the individual from which the data such as missing values and outliers have been removed. ~ 15. One of the determinations is performed. As a result, among the amino acid concentrations in the blood, 2 groups of NASH and non-NASH, 2 groups of NAFLD and non-NAFLD, 2 groups of fatty liver and non-fatty liver, or 2 of NASH and simple fatty liver Using the amino acid concentration useful for group discrimination, these two group discriminations can be performed with high accuracy. Also, NASH and non-NASH 2-group discrimination, NAFLD and non-NAFLD 2-group discrimination, fatty liver and non-fatty liver 2-group discrimination, NASH and simple fatty liver 2-group discrimination, or non-NAFLD and NASH and simplicity By using the discriminant value obtained by the multivariate discriminant useful for the 3-group discrimination of fatty liver, the 2-group discrimination or the 3-group discrimination can be accurately performed.

Here, the multivariate discriminant used in step SA13 is a logistic regression equation, a fractional equation, a linear discriminant equation, a multiple regression equation, an equation created by a support vector machine, an equation created by the Mahalanobis distance method, and a canonical discriminant. Any one of an expression created by analysis and an expression created by a decision tree may be used. As a result, NASH and non-NASH 2-group discrimination, NAFLD and non-NAFLD 2-group discrimination, fatty liver and non-fatty liver 2-group discrimination, NASH and simple fatty liver 2-group discrimination, or non-NAFLD and NASH and simple By using the discriminant value obtained by the multivariate discriminant useful for the 3-group discrimination of the fatty liver, these 2-group discrimination or 3-group discrimination can be performed with higher accuracy.

Specifically, the above-mentioned 11. The multivariate discriminant used in the discriminant may be a logistic regression equation including Glu, Gln, Gly, Ala, Val, Tyr as variables. Thereby, this two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination of NASH and non-NASH. In addition, the above-mentioned 12. The multivariate discriminant used in discriminating the above may be a logistic regression equation including Ser, Glu, Gly, Val, Tyr, and His as variables. This makes it possible to perform the two-group discrimination with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination of NAFLD and non-NAFLD. In addition, the above-mentioned 13. The multivariate discriminant used in this discrimination may be a logistic regression equation including Ser, Glu, Gly, Ala, Val, and Tyr as variables. Thus, the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between fatty liver and non-fatty liver. Further, the above-mentioned 14. The multivariate discriminant used in the discriminant may be a logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as variables. Thereby, this two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between NASH and simple fatty liver. Further, the above-mentioned 15. The multivariate discriminant used for discriminating is a logistic regression equation including Ser, Glu, Gly, Val, Tyr, and His as variables, and a logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as variables. Good. Thereby, this three-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the three-group discrimination of non-NAFLD, NASH, and simple fatty liver.

Each multivariate discriminant described above is a method described in International Publication No. 2004/052191 which is an international application by the present applicant or a method described in International Publication No. 2006/098192 which is an international application by the present applicant. It may be created by (multivariate discriminant creation processing described in the second embodiment to be described later). If the multivariate discriminant obtained by these methods is used, the multivariate discriminant is preferably used for the evaluation of the state of fatty liver disease regardless of the unit of amino acid concentration in the amino acid concentration data as input data. be able to.

[Second Embodiment]
[2-1. Outline of the present invention]
Here, for the outline of the fatty liver disease evaluation apparatus, fatty liver disease evaluation method, fatty liver disease evaluation program, recording medium, fatty liver disease evaluation system, and information communication terminal device according to the present invention, FIG. The description will be given with reference. FIG. 3 is a principle configuration diagram showing the basic principle of the present invention.

First, according to the present invention, the control unit obtains the amino acid concentration data of the evaluation target (for example, an individual such as an animal or a human) previously obtained with respect to the amino acid concentration value, and the multivariate discriminant stored in the storage unit for varying the amino acid concentration. Based on, a discriminant value that is the value of the multivariate discriminant is calculated (step S21).

Next, according to the present invention, at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis) is evaluated with respect to the evaluation target based on the discriminant value calculated in step S21 by the control unit. The state of fatty liver disease including one is evaluated (step S22).

As described above, according to the present invention, based on the amino acid concentration data to be evaluated and the multivariate discriminant including the amino acid concentration as a variable, the discriminant value that is the value of the multivariate discriminant is calculated, and the calculated discriminant value Based on the above, the status of fatty liver disease including at least one of fatty liver, NAFLD, and NASH is evaluated for each evaluation subject. Thereby, the state of fatty liver disease can be accurately evaluated using the discriminant value obtained by the multivariate discriminant including the amino acid concentration as a variable.

In step S21, at least one concentration value among Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser included in the amino acid concentration data. And a multivariate discriminant including at least one of Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser as a variable, A discriminant value may be calculated, and in step S22, the state of NASH may be evaluated for the evaluation target based on the discriminant value calculated in step S21. Thereby, the NASH state can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the NASH state. Specifically, it may be determined whether the evaluation target is NASH or non-NASH based on the determination value. This makes it possible to accurately perform the two-group discrimination using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between NASH and non-NASH. The multivariate discriminant may be a logistic regression equation including Glu, Gln, Gly, Ala, Val, Tyr as variables. Thereby, this two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination of NASH and non-NASH.

In step S21, Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn included in the amino acid concentration data. At least one of the concentration values and at least one of Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn The discriminant value may be calculated based on a multivariate discriminant including the variable, and in step S22, the NAFLD state may be evaluated for the evaluation target based on the discriminant value calculated in step S21. Thereby, the NAFLD state can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the NAFLD state. Specifically, it may be determined whether the evaluation target is NAFLD or non-NAFLD based on the determination value. This makes it possible to accurately perform the two-group discrimination by using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between NAFLD and non-NAFLD. Note that the multivariate discriminant may be a logistic regression equation including Ser, Glu, Gly, Val, Tyr, and His as variables. This makes it possible to perform the two-group discrimination with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination of NAFLD and non-NAFLD.

In step S21, at least one of Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn included in the amino acid concentration data. Multivariate discriminant including a concentration value and at least one of Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn as a variable The discriminant value may be calculated based on the above, and in step S22, the state of fatty liver may be evaluated for the evaluation target based on the discriminant value calculated in step S21. Thereby, the state of fatty liver can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the state of fatty liver. Specifically, based on the discriminant value, it may be discriminated whether the evaluation target is fatty liver or non-fatty liver. This makes it possible to accurately perform the two-group discrimination using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between fatty liver and non-fatty liver. The multivariate discriminant may be a logistic regression equation including Ser, Glu, Gly, Ala, Val, and Tyr as variables. Thus, the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between fatty liver and non-fatty liver.

In step S21, at least one concentration value of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA included in the amino acid concentration data, and Gln, A discriminant value is calculated based on a multivariate discriminant including at least one of Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro and ABA as a variable, and step S22 Then, based on the discriminant value calculated in step S21, the state of NASH and NAFLD may be evaluated for each evaluation target. Thereby, the state of NASH and NAFLD can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the state of NASH and NAFLD. Specifically, it may be determined whether the evaluation target is NASH or “non-NASH and NAFLD” (simple fatty liver) based on the determination value. This makes it possible to accurately perform this two-group discrimination using a discriminant value obtained by a multivariate discriminant useful for two-group discrimination between NASH and simple fatty liver. Note that the multivariate discriminant may be a logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as variables. Thereby, this two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between NASH and simple fatty liver. Specifically, it may be determined whether the evaluation target is non-NAFLD, NASH, or “non-NASH and NAFLD” based on the determination value. This makes it possible to accurately perform this three-group discrimination by using the discriminant value obtained by the multivariate discriminant useful for the three-group discrimination of non-NAFLD, NASH, and simple fatty liver. The multivariate discriminant may be a logistic regression equation including Ser, Glu, Gly, Val, Tyr, and His as variables, and a logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as variables. Thereby, this three-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the three-group discrimination of non-NAFLD, NASH, and simple fatty liver.

Here, each multivariate discriminant described above is described in the method described in International Publication No. 2004/052191 which is an international application by the present applicant or International Publication No. 2006/098192 which is an international application by the present applicant. It may be created by a method (multivariate discriminant creation process described later). If the multivariate discriminant obtained by these methods is used, the multivariate discriminant is preferably used for the evaluation of the state of fatty liver disease regardless of the unit of amino acid concentration in the amino acid concentration data as input data. be able to.

Here, the outline of the multivariate discriminant creation process (step 1 to step 4) will be described in detail. Note that the processing described here is merely an example, and the method of creating the multivariate discriminant is not limited to this.

First, according to the present invention, the control unit creates a predetermined formula from the fatty liver disease state information stored in the storage unit including the amino acid concentration data and the fatty liver disease state index data relating to the index representing the state of fatty liver disease. Based on the technique, a candidate multivariate discriminant that is a candidate for the multivariate discriminant (for example, y = a ₁ x ₁ + a ₂ x ₂ +... + A _n x _n , y: fatty liver disease state index data, x _i : amino acid concentration data, a _i : constant, i = 1, 2,..., n) are created (step 1). Note that data having missing values or outliers may be removed from the fatty liver disease state information in advance.

In Step 1, a plurality of different formula creation methods (principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, logistic regression analysis, k-means method, cluster analysis, decision tree, from fatty liver disease state information A plurality of candidate multivariate discriminants may be created in combination. Specifically, fatty liver disease state which is multivariate data composed of amino acid concentration data and fatty liver disease state index data obtained by analyzing blood obtained from many normal groups and fatty liver disease groups A plurality of groups of candidate multivariate discriminants may be created in parallel for information using a plurality of different algorithms. For example, two different candidate multivariate discriminants may be created by performing discriminant analysis and logistic regression analysis simultaneously using different algorithms. Candidate multivariate discrimination is performed by converting fatty liver disease state information using candidate multivariate discriminants created by principal component analysis and performing discriminant analysis on the converted fatty liver disease state information An expression may be created. Thereby, finally, an appropriate multivariate discriminant suitable for the diagnostic condition can be created.

Here, the candidate multivariate discriminant created using principal component analysis is a linear expression composed of amino acid variables that maximizes the variance of all amino acid concentration data. In addition, the candidate multivariate discriminant created using discriminant analysis is a higher-order formula (index or Including logarithm). The candidate multivariate discriminant created using the support vector machine is a higher-order formula (including a kernel function) made up of amino acid variables that maximizes the boundary between groups. In addition, the candidate multivariate discriminant created using multiple regression analysis is a higher-order expression composed of amino acid variables that minimizes the sum of distances from all amino acid concentration data. A candidate multivariate discriminant created using logistic regression analysis is a fractional expression having a natural logarithm as a term, which is a linear expression composed of amino acid variables that maximize the likelihood. The k-means method searches k neighborhoods of each amino acid concentration data, defines the largest group among the groups to which the neighboring points belong as the group to which the data belongs, This is a method of selecting an amino acid variable that best matches the group to which the group belongs. Cluster analysis is a method of clustering (grouping) points that are closest to each other in all amino acid concentration data. The decision tree is a technique for predicting a group of amino acid concentration data based on patterns that can be taken by amino acid variables having higher ranks by adding ranks to amino acid variables.

Returning to the description of the multivariate discriminant creation process, the present invention verifies (mutually verifies) the candidate multivariate discriminant created in step 1 based on a predetermined verification method in the control unit (step 2). The candidate multivariate discriminant is verified for each candidate multivariate discriminant created in step 1.

In step 2, the discrimination rate, sensitivity, specificity, information criterion of the candidate multivariate discriminant based on at least one of the bootstrap method, holdout method, N-fold method, leave one out method, etc. The verification may be performed on at least one of ROC_AUC (area under the curve of the receiver characteristic curve) and the like. Thereby, a candidate multivariate discriminant with high predictability or robustness in consideration of fatty liver disease state information and diagnostic conditions can be created.

Here, the discrimination rate is the correct ratio of the fatty liver disease state evaluated by the present invention in all input data. Sensitivity is the correct proportion of the fatty liver disease state evaluated in the present invention among the fatty liver disease states described in the input data. The specificity is the correct proportion of the fatty liver disease state evaluated in the present invention among the normal fatty liver disease described in the input data. The information criterion is the number of amino acid variables of the candidate multivariate discriminant prepared in Step 1, the state of fatty liver disease evaluated in the present invention, and the state of fatty liver disease described in the input data. It is the sum of the differences. ROC_AUC (area under the curve of the receiver characteristic curve) is a receiver characteristic curve (ROC) that is a curve created by plotting (x, y) = (1−specificity, sensitivity) on a two-dimensional coordinate. ), The value of ROC_AUC is 1 in complete discrimination, and the closer this value is to 1, the higher the discriminability. The predictability is an average of the discrimination rate, sensitivity, and specificity obtained by repeating the verification of the candidate multivariate discriminant. Robustness is the variance of discrimination rate, sensitivity, and specificity obtained by repeating verification of candidate multivariate discriminants.

Returning to the description of the multivariate discriminant creation process, the present invention allows the control unit to select a candidate multivariate discriminant variable from the verification result in step 2 based on a predetermined variable selection method (however, the process The variable of the candidate multivariate discriminant may be selected based on a predetermined variable selection method without considering the verification result in 2.), fatty liver disease state used when creating the candidate multivariate discriminant A combination of amino acid concentration data included in the information is selected (step 3). Amino acid variables are selected for each candidate multivariate discriminant created in step 1. Thereby, the amino acid variable of a candidate multivariate discriminant can be selected appropriately. Then, Step 1 is executed again using the fatty liver disease state information including the amino acid concentration data selected in Step 3.

In step 3, the amino acid variable of the candidate multivariate discriminant may be selected from the verification result in step 2 based on at least one of stepwise method, best path method, neighborhood search method, and genetic algorithm. .

Here, the best path method is a method of selecting amino acid variables by sequentially reducing amino acid variables included in the candidate multivariate discriminant one by one and optimizing the evaluation index given by the candidate multivariate discriminant. is there.

Returning to the description of the multivariate discriminant creation process, the present invention repeatedly executes the above-described step 1, step 2 and step 3 in the control unit, and a plurality of candidate multivariate discriminants based on the verification results accumulated thereby. A multivariate discriminant is created by selecting a candidate multivariate discriminant to be adopted as a multivariate discriminant from the equations (step 4). In selecting candidate multivariate discriminants, for example, selecting the optimal one from among candidate multivariate discriminants created by the same formula creation method, and selecting the optimum from all candidate multivariate discriminants Sometimes there is a choice.

As described above, in the multivariate discriminant creation process, based on fatty liver disease state information, candidate multivariate discriminant creation, candidate multivariate discriminant verification, and candidate multivariate discriminant variable selection By executing the processing related to systematization (systematization) in a series of flows, it is possible to create a multivariate discriminant that is optimal for evaluating the state of fatty liver disease. In other words, in the multivariate discriminant creation process, the amino acid concentration is used for multivariate statistical analysis, and the variable selection method and cross-validation are combined to select the optimal and robust variable set. Extract the variable discriminant. As the multivariate discriminant, logistic regression, linear discrimination, support vector machine, Mahalanobis distance method, multiple regression analysis, cluster analysis, and the like can be used.

[2-2. System configuration]
Here, the configuration of the fatty liver disease evaluation system according to the second embodiment (hereinafter sometimes referred to as the present system) will be described with reference to FIGS. 4 to 20. This system is merely an example, and the present invention is not limited to this.

First, the overall configuration of this system will be described with reference to FIG. 4 and FIG. FIG. 4 is a diagram showing an example of the overall configuration of the present system. FIG. 5 is a diagram showing another example of the overall configuration of the present system. As shown in FIG. 4, the present system includes a fatty liver disease evaluation apparatus 100 that evaluates the state of fatty liver disease for each evaluation object, and a client apparatus 200 that provides amino acid concentration data of the evaluation object relating to the amino acid concentration value. (Corresponding to the information communication terminal device of the present invention) is connected to be communicable via the network 300.

In addition to the fatty liver disease evaluation apparatus 100 and the client apparatus 200, this system uses fatty liver disease used when creating a multivariate discriminant in the fatty liver disease evaluation apparatus 100, as shown in FIG. The database apparatus 400 that stores state information, a multivariate discriminant used for evaluating the state of fatty liver disease, and the like may be configured to be communicably connected via the network 300. Accordingly, the fatty liver disease state is transferred from the fatty liver disease evaluation device 100 to the client device 200 or the database device 400 or from the client device 200 or the database device 400 to the fatty liver disease evaluation device 100 via the network 300. Information about Here, the information relating to the state of fatty liver disease is information relating to values measured for specific items relating to the state of fatty liver disease in organisms including humans. Information regarding the state of fatty liver disease is generated by the fatty liver disease evaluation device 100, the client device 200, and other devices (for example, various measuring devices), and is mainly stored in the database device 400.

Next, the configuration of the fatty liver disease evaluation apparatus 100 of this system will be described with reference to FIGS. FIG. 6 is a block diagram showing an example of the configuration of the fatty liver disease evaluation apparatus 100 of the present system, and conceptually shows only the portion related to the present invention in the configuration.

The fatty liver disease evaluation apparatus 100 includes a control unit 102 such as a CPU that comprehensively controls the fatty liver disease evaluation apparatus, a communication device such as a router, and a wired or wireless communication line such as a dedicated line. A communication interface unit 104 that connects the fatty liver disease evaluation device to the network 300 so as to be communicable, a storage unit 106 that stores various databases, tables, files, and the like, and an input / output interface that connects to the input device 112 and the output device 114 And these units are communicably connected via an arbitrary communication path. Here, the fatty liver disease evaluation apparatus 100 may be configured in the same housing as various analysis apparatuses (for example, an amino acid analyzer or the like). Further, the specific form of dispersion / integration of the fatty liver disease evaluation apparatus 100 is not limited to that shown in the figure, and all or a part thereof may be determined in arbitrary units according to various additions or according to functional load. It can be configured functionally or physically distributed and integrated. In other words, the embodiments of this specification may be implemented in any combination, and the embodiments may be selectively implemented. For example, a part of the processing may be realized using CGI (Common Gateway Interface).

The storage unit 106 is a storage means, and for example, a memory device such as a RAM / ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used. The storage unit 106 stores a computer program for giving instructions to the CPU and performing various processes in cooperation with an OS (Operating System). As shown in the figure, the storage unit 106 includes a user information file 106a, an amino acid concentration data file 106b, a fatty liver disease state information file 106c, a designated fatty liver disease state information file 106d, and multivariate discriminant-related information. A database 106e, a discriminant value file 106f, and an evaluation result file 106g are stored.

The user information file 106a stores user information related to users. FIG. 7 is a diagram illustrating an example of information stored in the user information file 106a. As shown in FIG. 7, the information stored in the user information file 106a includes a user ID for uniquely identifying a user and authentication for whether or not the user is a valid person. A user password, a user name, an affiliation ID for uniquely identifying the affiliation to which the user belongs, a department ID for uniquely identifying the department to which the user belongs, The department name and the user's e-mail address are associated with each other.

Referring back to FIG. 6, the amino acid concentration data file 106b stores amino acid concentration data relating to amino acid concentration values. FIG. 8 is a diagram showing an example of information stored in the amino acid concentration data file 106b. As shown in FIG. 8, the information stored in the amino acid concentration data file 106b is configured by associating an individual number for uniquely identifying an individual (sample) to be evaluated with amino acid concentration data. Yes. Here, in FIG. 8, the amino acid concentration data is treated as a numerical value, that is, a continuous scale, but the amino acid concentration data may be a nominal scale or an order scale. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state. In addition, amino acid concentration data includes other biological information (for example, biological metabolites such as sugars, lipids, proteins, peptides, minerals, hormones, etc. You may combine biomarkers such as habits, exercise habits, obesity levels, and disease histories.

Returning to FIG. 6, the fatty liver disease state information file 106c stores fatty liver disease state information used when creating a multivariate discriminant. FIG. 9 is a diagram illustrating an example of information stored in the fatty liver disease state information file 106c. As shown in FIG. 9, information stored in the fatty liver disease state information file 106c includes an individual number and an index (index T ₁ , index T ₂ , index T ₃ ... ) Related fatty acid disease state index data (T) and amino acid concentration data are associated with each other. Here, in FIG. 9, fatty liver disease state index data and amino acid concentration data are treated as numerical values (that is, a continuous scale), but fatty liver disease state index data and amino acid concentration data may be nominal scales or order scales. . In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state. The fatty liver disease state index data is a known single state index serving as a marker for the state of fatty liver disease, and numerical data may be used.

Referring back to FIG. 6, the designated fatty liver disease state information file 106d stores the fatty liver disease state information designated by the fatty liver disease state information designation unit 102g described later. FIG. 10 is a diagram illustrating an example of information stored in the designated fatty liver disease state information file 106d. As shown in FIG. 10, the information stored in the designated fatty liver disease state information file 106d is obtained by associating an individual number, designated fatty liver disease state index data, and designated amino acid concentration data with each other. It is configured.

Returning to FIG. 6, the multivariate discriminant-related information database 106e includes a candidate multivariate discriminant file 106e1 for storing the candidate multivariate discriminant created by the candidate multivariate discriminant-preparing part 102h1 described below, and a candidate multivariate discriminant file 106e1 described later. Selected fatty liver disease state information for storing fatty liver disease state information including a combination of a verification result file 106e2 for storing the verification result in the discriminant verification unit 102h2 and amino acid concentration data selected by the variable selection unit 102h3 to be described later A file 106e3 and a multivariate discriminant file 106e4 that stores the multivariate discriminant created by the multivariate discriminant-preparing part 102h described later.

The candidate multivariate discriminant file 106e1 stores the candidate multivariate discriminant created by the candidate multivariate discriminant creation unit 102h1 described later. FIG. 11 is a diagram illustrating an example of information stored in the candidate multivariate discriminant file 106e1. As shown in FIG. 11, information stored in the candidate multivariate discriminant file 106e1 includes a rank, a candidate multivariate discriminant (in FIG. 11, F ₁ (Gly, Leu, Phe,...)) And F _2. (Gly, Leu, Phe,...), F ₃ (Gly, Leu, Phe,...)) Are associated with each other.

Returning to FIG. 6, the verification result file 106e2 stores the verification result in the candidate multivariate discriminant verification unit 102h2 described later. FIG. 12 is a diagram illustrating an example of information stored in the verification result file 106e2. As shown in FIG. 12, the information stored in the verification result file 106e2 includes rank, candidate multivariate discriminant (in FIG. 12, F _k (Gly, Leu, Phe,...) And F _m (Gly, Le, Phe,...), _Fl (Gly, Leu, Phe,...)) And the verification results of each candidate multivariate discriminant (for example, the evaluation value of each candidate multivariate discriminant). They are related to each other.

Referring back to FIG. 6, the selected fatty liver disease state information file 106e3 stores fatty liver disease state information including a combination of amino acid concentration data corresponding to variables selected by the variable selection unit 102h3 described later. FIG. 13 is a diagram illustrating an example of information stored in the selected fatty liver disease state information file 106e3. As shown in FIG. 13, the information stored in the selected fatty liver disease state information file 106e3 includes an individual number, fatty liver disease state index data designated by the fatty liver disease state information designation unit 102g described later, Amino acid concentration data selected by a variable selection unit 102h3 described later is associated with each other.

Returning to FIG. 6, the multivariate discriminant file 106e4 stores the multivariate discriminant created by the multivariate discriminant-preparing part 102h described later. FIG. 14 is a diagram illustrating an example of information stored in the multivariate discriminant file 106e4. As shown in FIG. 14, the information stored in the multivariate discriminant file 106e4 includes the rank, the multivariate discriminant (in FIG. 14, F _p (Phe,...) And F _p (Gly, Leu, Phe). ), F _k (Gly, Leu, Phe,...)), A threshold corresponding to each formula creation method, a verification result of each multivariate discriminant (for example, an evaluation value of each multivariate discriminant), Are related to each other.

Returning to FIG. 6, the discriminant value file 106f stores the discriminant value calculated by the discriminant value calculator 102i described later. FIG. 15 is a diagram illustrating an example of information stored in the discrimination value file 106f. As shown in FIG. 15, information stored in the discriminant value file 106f includes an individual number for uniquely identifying an individual (sample) to be evaluated and a rank (for uniquely identifying a multivariate discriminant). Number) and the discriminant value are associated with each other.

Returning to FIG. 6, the evaluation result file 106g stores an evaluation result in a discriminant value criterion-evaluating unit 102j described later (specifically, a discrimination result in a discriminant value criterion-discriminating unit 102j1 described later). FIG. 16 is a diagram illustrating an example of information stored in the evaluation result file 106g. Information stored in the evaluation result file 106g includes an individual number for uniquely identifying an individual (sample) to be evaluated, amino acid concentration data of the evaluation target acquired in advance, and a discriminant value calculated by a multivariate discriminant. And the evaluation result regarding the evaluation of the state of fatty liver disease are associated with each other.

Referring back to FIG. 6, the storage unit 106 stores various types of Web data for providing the Web site to the client device 200, CGI programs, and the like as other information in addition to the information described above. The Web data includes data for displaying various Web pages to be described later, and these data are formed as text files described in HTML or XML, for example. In addition, a part file, a work file, and other temporary files for creating Web data are also stored in the storage unit 106. The storage unit 106 stores audio for transmission to the client device 200 as an audio file such as WAVE format or AIFF format, and stores still images or moving images as image files such as JPEG format or MPEG2 format as necessary. Can be stored.

The communication interface unit 104 mediates communication between the fatty liver disease evaluation device 100 and the network 300 (or a communication device such as a router). That is, the communication interface unit 104 has a function of communicating data with other terminals via a communication line.

The input / output interface unit 108 is connected to the input device 112 and the output device 114. Here, in addition to a monitor (including a home television), a speaker or a printer can be used as the output device 114 (hereinafter, the output device 114 may be described as the monitor 114). As the input device 112, a monitor that realizes a pointing device function in cooperation with a mouse can be used in addition to a keyboard, a mouse, and a microphone.

The control unit 102 has an internal memory for storing a control program such as an OS (Operating System), a program defining various processing procedures, and necessary data, and performs various information processing based on these programs. Execute. As shown in the figure, the control unit 102 is roughly divided into a request interpretation unit 102a, a browsing processing unit 102b, an authentication processing unit 102c, an e-mail generation unit 102d, a Web page generation unit 102e, a reception unit 102f, and fatty liver disease state information designation. Unit 102g, multivariate discriminant creation unit 102h, discriminant value calculator 102i, discriminant value criterion-evaluator 102j, result output unit 102k, and transmitter 102m. The control unit 102 removes data with missing values, removes data with many outliers, and lacks data with respect to fatty liver disease state information transmitted from the database device 400 and amino acid concentration data transmitted from the client device 200. Data processing such as removal of variables with a lot of valued data is also performed.

The request interpretation unit 102a interprets the request content from the client device 200 or the database device 400, and passes the processing to each unit of the control unit 102 according to the interpretation result. Upon receiving browsing requests for various screens from the client device 200, the browsing processing unit 102b generates and transmits Web data for these screens. Upon receiving an authentication request from the client device 200 or the database device 400, the authentication processing unit 102c makes an authentication determination. The e-mail generation unit 102d generates an e-mail including various types of information. The web page generation unit 102e generates a web page that the user browses on the client device 200.

The receiving unit 102f receives information (specifically, amino acid concentration data, fatty liver disease state information, multivariate discriminant, etc.) transmitted from the client device 200 or the database device 400 via the network 300. The fatty liver disease state information designating unit 102g designates target fatty liver disease state index data and amino acid concentration data when creating a multivariate discriminant.

The multivariate discriminant creating unit 102h creates a multivariate discriminant based on the fatty liver disease state information received by the receiving unit 102f and the fatty liver disease state information specified by the fatty liver disease state information specifying unit 102g. . Specifically, the multivariate discriminant-preparing part 102h repeatedly executes the candidate multivariate discriminant-preparing part 102h1, the candidate multivariate discriminant-verifying part 102h2, and the variable selecting part 102h3 from the fatty liver disease state information. A multivariate discriminant is created by selecting a candidate multivariate discriminant to be adopted as a multivariate discriminant from a plurality of candidate multivariate discriminants based on the accumulated verification results.

When the multivariate discriminant is stored in a predetermined storage area of the storage unit 106 in advance, the multivariate discriminant-preparing unit 102h selects a desired multivariate discriminant from the storage unit 106, A multivariate discriminant may be created. In addition, the multivariate discriminant creation unit 102h creates a multivariate discriminant by selecting and downloading a desired multivariate discriminant from another computer device (for example, the database device 400) that stores the multivariate discriminant in advance. May be.

Here, the configuration of the multivariate discriminant-preparing part 102h will be described with reference to FIG. FIG. 17 is a block diagram showing the configuration of the multivariate discriminant-preparing part 102h, and conceptually shows only the part related to the present invention. The multivariate discriminant creation unit 102h further includes a candidate multivariate discriminant creation unit 102h1, a candidate multivariate discriminant verification unit 102h2, and a variable selection unit 102h3. The candidate multivariate discriminant-preparing part 102h1 creates a candidate multivariate discriminant that is a candidate for the multivariate discriminant from the fatty liver disease state information based on a predetermined formula creation method. In addition, the candidate multivariate discriminant-preparing part 102h1 may create a plurality of candidate multivariate discriminants from a fatty liver disease state information by using a plurality of different formula creation methods. The candidate multivariate discriminant verification unit 102h2 verifies the candidate multivariate discriminant created by the candidate multivariate discriminant creation unit 102h1 based on a predetermined verification method. Note that the candidate multivariate discriminant verification unit 102h2 determines the discriminant rate, sensitivity, and specificity of the candidate multivariate discriminant based on at least one of the bootstrap method, holdout method, N-fold method, and leave one out method. , Information criterion, ROC_AUC (area under the receiver characteristic curve) may be verified. When the variable selection unit 102h3 creates a candidate multivariate discriminant by selecting a variable of the candidate multivariate discriminant based on a predetermined variable selection method from the verification result in the candidate multivariate discriminant verification unit 102h2. A combination of amino acid concentration data included in the fatty liver disease state information to be used is selected. Note that the variable selection unit 102h3 may select a variable of the candidate multivariate discriminant from the verification result based on at least one of the stepwise method, the best path method, the neighborhood search method, and the genetic algorithm.

Returning to FIG. 6, the discriminant value calculation unit 102 i determines the multivariate discriminant based on the multivariate discriminant created by the multivariate discriminant creation unit 102 h and the evaluation target amino acid concentration data received by the receiver 102 f. The discriminant value which is a value is calculated. Multivariate discriminants are logistic regression formula, fractional formula, linear discriminant formula, multiple regression formula, formula created by support vector machine, formula created by Mahalanobis distance method, formula created by canonical discriminant analysis. Any one of the expressions created by the decision tree may be used.

Specifically, when the discriminant value criterion-evaluating unit 102j evaluates the NASH state (specifically, when the discriminant value criterion-discriminating unit 102j1 described later determines whether it is NASH or non-NASH). The value calculation unit 102i includes at least one concentration value among Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser included in the amino acid concentration data. And a multivariate discriminant including at least one of Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser as a variable, The discrimination value may be calculated. When the discrimination value criterion discrimination unit 102j1 determines whether or not NASH or non-NASH, the multivariate discriminant may be a logistic regression equation including Glu, Gln, Gly, Ala, Val, Tyr as variables.

Further, when the NAFLD state is evaluated by the discriminant value criterion evaluation unit 102j (specifically, when the discriminant value criterion discriminator 102j1 described later determines whether NAFLD or non-NAFLD), the discriminant value calculation unit 102i is at least 1 of Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn included in the amino acid concentration data. One concentration value and at least one of Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn as a variable The discriminant value may be calculated on the basis of the multivariate discriminant included. When the discriminant value criterion discriminating unit 102j1 determines whether or not NAFLD or non-NAFLD, the multivariate discriminant may be a logistic regression equation including Ser, Glu, Gly, Val, Tyr, and His as variables.

Also, when the discriminant value criterion-evaluating unit 102j evaluates the state of fatty liver (specifically, when the discriminant value criterion-discriminating unit 102j1 described later determines whether the liver is fatty liver or non-fatty liver). The value calculation unit 102i includes at least one of Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, and Orn included in the amino acid concentration data. Multivariate discriminant including a concentration value and at least one of Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn as a variable The discriminant value may be calculated based on the above. When the discriminant value criterion discriminating unit 102j1 discriminates whether it is fatty liver or non-fatty liver, the multivariate discriminant is a logistic regression equation including Ser, Glu, Gly, Ala, Val, Tyr as variables. Good.

Further, when the discriminant value criterion evaluation unit 102j evaluates the state of NASH and NAFLD (specifically, the discriminant value criterion discriminator 102j1 described later is NASH or “non-NASH and NAFLD” (simple fatty liver). When determining whether or not it is non-NAFLD, NASH, or “non-NASH and NAFLD”), the discriminant value calculation unit 102i includes Gln, Glu, and Gly included in the amino acid concentration data. , Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, ABA, and Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe , Met, Ile, Pro, Multivariate discriminant including at least one of ABA as a variable Based on may calculate the discrimination value. When the discriminant value criterion discriminating unit 102j1 discriminates whether it is NASH or “non-NASH and NAFLD” (simple fatty liver), the multivariate discriminant is Asn, Gln, Gly, Ala, Cit, Met. May be a logistic regression equation including as a variable. Further, when the discriminant value criterion discriminating unit 102j1 discriminates whether or not it is non-NAFLD, NASH, or “non-NASH and NAFLD”, the multivariate discriminant uses Ser, Glu, Gly, Val, Tyr, and His as variables. And a logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as variables.

Based on the discriminant value calculated by the discriminant value calculating unit 102i, the discriminant value criterion-evaluating unit 102j is evaluated for fatty liver disease (specifically, at least one of NASH, NAFLD, and fatty liver). Assess the condition. The discrimination value criterion evaluation unit 102j further includes a discrimination value criterion discrimination unit 102j1. Here, the configuration of the discriminant value criterion-evaluating unit 102j will be described with reference to FIG. FIG. 18 is a block diagram showing the configuration of the discriminant value criterion-evaluating unit 102j, and conceptually shows only the portion related to the present invention. Based on the discriminant value, the discriminant value criterion discriminating unit 102j1 discriminates whether the evaluation target is NASH or non-NASH, discriminates whether it is NAFLD or non-NAFLD, fatty liver or non-fatty liver Whether it is NASH or “non-NASH and NAFLD” (simple fatty liver), or whether it is non-NAFLD, NASH, or “non-NASH and NAFLD” To do. Specifically, the discriminant value criterion discriminating unit 102j1 executes any one of these discriminators for each evaluation target by comparing the discriminant value with a preset threshold value (cut-off value). .

Returning to FIG. 6, the result output unit 102k displays the processing results in the respective processing units of the control unit 102 (evaluation results in the discrimination value criterion evaluation unit 102j (specifically, discrimination results in the discrimination value criterion discrimination unit 102j1)). Output) to the output device 114.

The transmission unit 102m transmits the evaluation result to the client apparatus 200 that is the transmission source of the amino acid concentration data to be evaluated, or the multivariate discriminant created by the fatty liver disease evaluation apparatus 100 to the database apparatus 400 Send evaluation results.

Next, the configuration of the client device 200 of this system will be described with reference to FIG. FIG. 19 is a block diagram showing an example of the configuration of the client apparatus 200 of the present system, and conceptually shows only the portion related to the present invention in the configuration.

The client device 200 includes a control unit 210, a ROM 220, an HD 230, a RAM 240, an input device 250, an output device 260, an input / output IF 270, and a communication IF 280. These units are communicably connected via an arbitrary communication path. Has been.

The control unit 210 includes a web browser 211, an electronic mailer 212, a reception unit 213, and a transmission unit 214. The web browser 211 performs browse processing for interpreting the web data and displaying the interpreted web data on a monitor 261 described later. The Web browser 211 may be plugged in with various software such as a stream player having a function of receiving, displaying, and feeding back a stream video. The electronic mailer 212 transmits and receives electronic mail according to a predetermined communication protocol (for example, SMTP (Simple Mail Transfer Protocol), POP3 (Post Office Protocol version 3), etc.). The receiving unit 213 receives various information such as the evaluation result transmitted from the fatty liver disease evaluation apparatus 100 via the communication IF 280. The transmission unit 214 transmits various types of information such as evaluation target amino acid concentration data to the fatty liver disease evaluation apparatus 100 via the communication IF 280.

The input device 250 is a keyboard, a mouse, a microphone, or the like. A monitor 261, which will be described later, also realizes a pointing device function in cooperation with the mouse. The output device 260 is an output unit that outputs information received via the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. In addition, the output device 260 may be provided with a speaker or the like. The input / output IF 270 is connected to the input device 250 and the output device 260.

The communication IF 280 connects the client device 200 and the network 300 (or a communication device such as a router) so that they can communicate with each other. In other words, the client device 200 is connected to the network 300 via a communication device such as a modem, TA, or router and a telephone line, or via a dedicated line. Thereby, the client apparatus 200 can access the fatty liver disease evaluation apparatus 100 according to a predetermined communication protocol.

Here, an information processing device (for example, a known personal computer, workstation, home game device, Internet TV, PHS terminal, portable terminal, mobile object) connected with peripheral devices such as a printer, a monitor, and an image scanner as necessary. The client device 200 may be realized by installing software (including programs, data, and the like) that realizes a Web data browsing function and an e-mail function in a communication terminal / information processing terminal such as a PDA).

Further, the control unit 210 of the client device 200 may be realized by a CPU and a program that is interpreted and executed by the CPU and all or any part of the processing performed by the control unit 210. The ROM 220 or the HD 230 stores computer programs for giving instructions to the CPU and performing various processes in cooperation with an OS (Operating System). The computer program is executed by being loaded into the RAM 240, and constitutes the control unit 210 in cooperation with the CPU. Further, the computer program may be recorded in an application program server connected to the client apparatus 200 via an arbitrary network, and the client apparatus 200 may download all or a part thereof as necessary. . In addition, all or any part of the processing performed by the control unit 210 may be realized by hardware such as wired logic.

Next, the network 300 of this system will be described with reference to FIGS. The network 300 has a function of connecting the fatty liver disease evaluation apparatus 100, the client apparatus 200, and the database apparatus 400 so that they can communicate with each other. For example, the network 300 is connected to the Internet, an intranet, a LAN (including both wired / wireless), and the like. is there. The network 300 includes a VAN, a personal computer communication network, a public telephone network (including both analog / digital), a dedicated line network (including both analog / digital), a CATV network, and a mobile line switching network. Or mobile packet switching network (including IMT2000 system, GSM (registered trademark) system or PDC / PDC-P system), wireless paging network, local wireless network such as Bluetooth (registered trademark), PHS network, satellite A communication network (including CS, BS or ISDB) may be used.

Next, the configuration of the database apparatus 400 of this system will be described with reference to FIG. FIG. 20 is a block diagram showing an example of the configuration of the database apparatus 400 of this system, and conceptually shows only the portion related to the present invention in the configuration.

The database device 400 is a fatty liver disease state information used when creating a multivariate discriminant in the fatty liver disease evaluation device 100 or the database device, a multivariate discriminant created in the fatty liver disease evaluation device 100, It has a function of storing evaluation results and the like in the fatty liver disease evaluation apparatus 100. As shown in FIG. 20, the database device 400 includes a control unit 402 such as a CPU that comprehensively controls the database device, a communication device such as a router, and a wired or wireless communication circuit such as a dedicated line. A communication interface unit 404 that connects the apparatus to the network 300 to be communicable, a storage unit 406 that stores various databases, tables, and files (for example, files for Web pages), and an input unit that connects to the input unit 412 and the output unit 414. And an output interface unit 408. These units are communicably connected via an arbitrary communication path.

The storage unit 406 is a storage means, and for example, a memory device such as a RAM / ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used. The storage unit 406 stores various programs used for various processes. The communication interface unit 404 mediates communication between the database device 400 and the network 300 (or a communication device such as a router). That is, the communication interface unit 404 has a function of communicating data with other terminals via a communication line. The input / output interface unit 408 is connected to the input device 412 and the output device 414. Here, in addition to a monitor (including a home TV), a speaker or a printer can be used as the output device 414 (hereinafter, the output device 414 may be described as the monitor 414). In addition to the keyboard, mouse, and microphone, the input device 412 can be a monitor that realizes a pointing device function in cooperation with the mouse.

The control unit 402 has an internal memory for storing a control program such as an OS (Operating System), a program that defines various processing procedures, and necessary data, and performs various information processing based on these programs. Execute. As shown in the figure, the control unit 402 is roughly divided into a request interpreting unit 402a, a browsing processing unit 402b, an authentication processing unit 402c, an e-mail generating unit 402d, a Web page generating unit 402e, and a transmitting unit 402f.

The request interpretation unit 402a interprets the request content from the fatty liver disease evaluation apparatus 100, and passes the processing to each unit of the control unit 402 according to the interpretation result. Upon receiving browsing requests for various screens from the fatty liver disease evaluation apparatus 100, the browsing processing unit 402b generates and transmits Web data for these screens. The authentication processing unit 402c receives an authentication request from the fatty liver disease evaluation apparatus 100 and makes an authentication determination. The e-mail generation unit 402d generates an e-mail including various types of information. The web page generation unit 402e generates a web page that the user browses on the client device 200. The transmission unit 402f transmits various types of information such as fatty liver disease state information and multivariate discriminants to the fatty liver disease evaluation apparatus 100.

[2-3. Processing of this system]
Here, an example of a fatty liver disease evaluation service process performed by the present system configured as described above will be described with reference to FIG. FIG. 21 is a flowchart illustrating an example of fatty liver disease evaluation service processing.

In addition, the amino acid concentration data used in the present processing is analyzed by a specialist in the blood (including plasma, serum, etc.) collected in advance from an individual by a measuring method such as the following (A) or (B) or independently. It is related with the concentration value of the amino acid obtained as described above. Here, the unit of amino acid concentration may be obtained by, for example, molar concentration, weight concentration, or by adding / subtracting / subtracting an arbitrary constant to / from these concentrations.
(A) Plasma was separated from blood by centrifuging the collected blood sample. All plasma samples were stored frozen at −80 ° C. until the measurement of amino acid concentration. For amino acid concentration measurement, acetonitrile was added to remove protein, followed by precolumn derivatization using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and liquid chromatography mass spectrometry The amino acid concentration was analyzed by a total (LC-MS) (see International Publication No. 2003/069328 and International Publication No. 2005/116629).
(B) Plasma was separated from blood by centrifuging the collected blood sample. All plasma samples were stored frozen at −80 ° C. until the measurement of amino acid concentration. When measuring the amino acid concentration, sulfosalicylic acid was added to remove the protein, and then the amino acid concentration was analyzed by an amino acid analyzer based on the post-column derivatization method using a ninhydrin reagent.

First, when the user designates an address (such as a URL) of a Web site provided by the fatty liver disease evaluation apparatus 100 via the input device 250 on the screen displaying the Web browser 211, the client apparatus 200 causes the fatty liver disease to be displayed. Access the evaluation device 100. Specifically, when the user instructs to update the screen of the Web browser 211 of the client device 200, the Web browser 211 uses the predetermined communication protocol to specify the address of the Web site provided by the fatty liver disease evaluation device 100. By transmitting to the disease evaluation apparatus 100, a transmission request for a Web page corresponding to the amino acid concentration data transmission screen is made to the fatty liver disease evaluation apparatus 100 by routing based on the address.

Next, the fatty liver disease evaluation apparatus 100 receives the transmission from the client apparatus 200 at the request interpretation unit 102a, analyzes the contents of the transmission, and moves the processing to each unit of the control unit 102 according to the analysis result. Specifically, when the content of the transmission is a web page transmission request corresponding to the amino acid concentration data transmission screen, the fatty liver disease evaluation apparatus 100 is a predetermined memory stored in the storage unit 106 mainly in the browsing processing unit 102b. Web data for displaying the Web page stored in the area is acquired, and the acquired Web data is transmitted to the client device 200. More specifically, when there is a transmission request for a Web page corresponding to the amino acid concentration data transmission screen from the user, the fatty liver disease evaluation apparatus 100 first uses the user ID and the user password in the control unit 102. Is requested from the user. When the user ID and password are input, the fatty liver disease evaluation apparatus 100 causes the authentication processing unit 102c to input the input user ID and password and the user ID stored in the user information file 106a. And authentication with user password. And the fatty liver disease evaluation apparatus 100 transmits the web data for displaying the web page corresponding to an amino acid concentration data transmission screen to the client apparatus 200 by the browsing process part 102b only when authentication is possible. The client device 200 is identified by the IP address transmitted from the client device 200 together with the transmission request.

Next, the client apparatus 200 receives the Web data transmitted from the fatty liver disease evaluation apparatus 100 (for displaying a Web page corresponding to the amino acid concentration data transmission screen) by the receiving unit 213, and receives the received Web The data is interpreted by the Web browser 211, and an amino acid concentration data transmission screen is displayed on the monitor 261.

Next, when the user inputs / selects individual amino acid concentration data or the like via the input device 250 on the amino acid concentration data transmission screen displayed on the monitor 261, the client device 200 uses the transmission unit 214 to input information and By transmitting an identifier for specifying a selection item to fatty liver disease evaluation apparatus 100, amino acid concentration data of the individual to be evaluated is transmitted to fatty liver disease evaluation apparatus 100 (step SA21). The transmission of amino acid concentration data in step SA21 may be realized by an existing file transfer technique such as FTP.

Next, the fatty liver disease evaluation apparatus 100 interprets the request contents of the client device 200 by interpreting the identifier transmitted from the client device 200 by the request interpretation unit 102a, and evaluates the state of fatty liver disease. Multivariate discriminant (specifically, 2-group discrimination of NASH and non-NASH, 2-group discrimination of NAFLD and non-NAFLD, 2-group discrimination of fatty liver and non-fatty liver, 2-group discrimination of NASH and simple fatty liver, Alternatively, a transmission request for a multivariate discriminant for discriminating three groups of NASH, simple fatty liver, and non-NAFLD is made to the database apparatus 400.

Next, the database apparatus 400 interprets the transmission request from the fatty liver disease evaluation apparatus 100 by the request interpretation unit 402a and stores the multivariate discriminant (for example, updated latest data) stored in a predetermined storage area of the storage unit 406. Are transmitted to fatty liver disease evaluation apparatus 100 (step SA22).

For example, when it is determined whether or not NASH or non-NASH in step SA26, in step SA22, Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, A multivariate discriminant including at least one of Thr, Asn, and Ser as a variable is transmitted to the fatty liver disease evaluation apparatus 100. Further, when it is determined whether or not it is NAFLD or non-NAFLD in step SA26, in step SA22, Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, A multivariate discriminant including at least one of Trp, Lys, Orn, Ser, Thr, Asn as a variable is transmitted to fatty liver disease evaluation apparatus 100. When it is determined in step SA26 whether the liver is fatty liver or non-fatty liver, in step SA22, Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, A multivariate discriminant including at least one of Met, His, Trp, Asn, and Orn as a variable is transmitted to the fatty liver disease evaluation apparatus 100. Further, when determining whether or not it is NASH or simple fatty liver in step SA26, or when determining whether or not it is non-NAFLD, NASH, or simple fatty liver, in step SA22, Gln, Glu, A multivariate discriminant including at least one of Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA as a variable is transmitted to the fatty liver disease evaluation apparatus 100.

Next, the fatty liver disease evaluation apparatus 100 receives the individual amino acid concentration data transmitted from the client apparatus 200 and the multivariate discriminant transmitted from the database apparatus 400 by the receiving unit 102f, and receives the received amino acid concentration data. Are stored in a predetermined storage area of the amino acid concentration data file 106b, and the received multivariate discriminant is stored in a predetermined storage area of the multivariate discriminant file 106e4 (step SA23).

Next, in the fatty liver disease evaluation apparatus 100, the controller 102 removes data such as missing values and outliers from the individual amino acid concentration data received in step SA23 (step SA24).

Next, the fatty liver disease evaluation apparatus 100 uses the discriminant value calculation unit 102i to determine the amino acid concentration data of the individual from which data such as missing values and outliers have been removed in step SA24, and the multivariate discriminant received in step SA23. Based on the above, a discrimination value is calculated (step SA25).

Specifically, when determining whether or not NASH or non-NASH in step SA26, fatty liver disease evaluating apparatus 100 uses Gln, Glu, Pro included in the amino acid concentration data in discriminant value calculation unit 102i. , Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser, at least one concentration value, and Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val , Tyr, Phe, Met, His, Trp, Thr, Asn, Ser. The discriminant value is calculated based on a multivariate discriminant including at least one as a variable.

When determining whether NAFLD or non-NAFLD in step SA26, the fatty liver disease evaluation apparatus 100 uses the discriminant value calculation unit 102i to determine whether the Gln, Glu, Pro, Gly, At least one concentration value among Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn, and Gln, Glu, Pro, Gly, Ala, Cit, A discriminant value is calculated based on a multivariate discriminant including at least one of Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn as a variable.

When it is determined in step SA26 whether the liver is fatty liver or non-fatty liver, the fatty liver disease evaluation apparatus 100 uses the discriminant value calculation unit 102i to determine Thr, Ser, Glu, At least one concentration value among Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn, and Thr, Ser, Glu, Pro, Gly, Ala, Cit, A discriminant value is calculated based on a multivariate discriminant including at least one of Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, and Orn as a variable.

Further, when determining whether or not it is NASH or simple fatty liver in Step SA26, or when determining whether or not it is non-NAFLD, NASH, or simple fatty liver in Step SA26, In the liver disease evaluation apparatus 100, the discriminant value calculation unit 102i uses at least one of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA included in the amino acid concentration data. Based on a multivariate discriminant including one concentration value and at least one of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, ABA as a variable, A discrimination value is calculated.

Next, the fatty liver disease evaluation apparatus 100 compares the discriminant value calculated in step SA25 with a preset threshold value (cut-off value) in the discriminant value criterion discriminating unit 102j1, so that NASH or Whether it is non-NASH, whether it is NAFLD or non-NAFLD, whether it is fatty liver or non-fatty liver, NASH or simple fatty liver (non-NASH and NAFLD) Or whether it is non-NAFLD, NASH, or simple fatty liver (non-NASH and NAFLD), and the determination result is stored in a predetermined storage area of the evaluation result file 106g. (Step SA26).

Next, fatty liver disease evaluation apparatus 100 transmits the determination result obtained in step SA26 to client apparatus 200 and database apparatus 400 that are the transmission source of amino acid concentration data, in transmission unit 102m (step SA27). Specifically, first, fatty liver disease evaluation apparatus 100 creates a web page for displaying a discrimination result in web page generation unit 102e, and stores web data corresponding to the created web page in storage unit 106. Store in a predetermined storage area. Next, after the user inputs a predetermined URL to the Web browser 211 of the client device 200 via the input device 250 and performs the above-described authentication, the client device 200 sends a request for browsing the Web page to the fatty liver disease evaluation device. To 100. Subsequently, in the fatty liver disease evaluation apparatus 100, the browsing processing unit 102b interprets the browsing request transmitted from the client device 200, and stores Web data corresponding to the Web page for displaying the determination result in the storage unit 106. Read from the storage area. The fatty liver disease evaluation apparatus 100 transmits the read Web data to the client apparatus 200 and transmits the Web data or the determination result to the database apparatus 400 by the transmission unit 102m.

Here, in Step SA27, the fatty liver disease evaluation apparatus 100 may notify the user client apparatus 200 of the determination result by e-mail at the control unit 102. Specifically, first, the fatty liver disease evaluation apparatus 100 refers to the user information stored in the user information file 106a based on the user ID or the like according to the transmission timing in the e-mail generation unit 102d. Get the user's email address. Subsequently, the fatty liver disease evaluation apparatus 100 uses the e-mail generation unit 102d to generate data related to the e-mail including the name and determination result of the user with the acquired e-mail address as the destination. Subsequently, fatty liver disease evaluation apparatus 100 transmits the generated data to user's client apparatus 200 by transmission unit 102m.

In step SA27, fatty liver disease evaluation apparatus 100 may transmit the discrimination result to user's client apparatus 200 using an existing file transfer technique such as FTP.

Returning to the description of FIG. 21, in the database device 400, the control unit 402 receives the discrimination result or Web data transmitted from the fatty liver disease evaluation device 100, and stores the received discrimination result or Web data in the storage unit 406. Is stored (accumulated) in the storage area (step SA28).

The client device 200 receives the Web data transmitted from the fatty liver disease evaluation device 100 by the receiving unit 213, interprets the received Web data by the Web browser 211, and stores the individual determination result. The page screen is displayed on the monitor 261 (step SA29). When the determination result is transmitted from the fatty liver disease evaluation apparatus 100 by e-mail, the client apparatus 200 uses the known function of the electronic mailer 212 to send the e-mail transmitted from the fatty liver disease evaluation apparatus 100. Is received at an arbitrary timing, and the received electronic mail is displayed on the monitor 261.

As described above, the user browses the Web page displayed on the monitor 261, thereby “determining whether or not NASH or non-NASH”, “determining whether or not NAFLD or non-NAFLD”, “ "Determining whether it is fatty liver or non-fatty liver", "Determining whether it is NASH or simple fatty liver", or "Determining whether it is non-NAFLD, NASH, or simple fatty liver" Can be confirmed. Note that the user may print the display content of the Web page displayed on the monitor 261 with the printer 262.

Further, when the determination result is transmitted from the fatty liver disease evaluation apparatus 100 by e-mail, the user browses the e-mail displayed on the monitor 261 to indicate whether “NASH or non-NASH”. "Determination of whether it is NAFLD or non-NAFLD", "determination of whether it is fatty liver or non-fatty liver", "determination of whether it is NASH or simple fatty liver" Or, it is possible to confirm the discrimination result of the individual regarding “discrimination of whether or not non-NAFLD, NASH, or simple fatty liver”. The user may print the content of the e-mail displayed on the monitor 261 with the printer 262.

This completes the explanation of the fatty liver disease evaluation service process.

[2-4. Summary of Second Embodiment and Other Embodiments]
As described above in detail, according to the fatty liver disease evaluation system, the client device 200 transmits the individual amino acid concentration data to the fatty liver disease evaluation device 100, and the database device 400 includes the fatty liver disease evaluation device. In response to a request from 100, a multivariate discriminant for discrimination between NASH and non-NASH, a multivariate discriminant for discrimination between NAFLD and non-NAFLD, a multivariate discriminant for discrimination between fatty liver and non-fatty liver, NASH And a multivariate discriminant for discriminating simple fatty liver, or a multivariate discriminant for discriminating non-NAFLD, NASH, and simple fatty liver are transmitted to fatty liver disease evaluation apparatus 100. The fatty liver disease evaluation apparatus 100 (1) receives the amino acid concentration data from the client device 200 and receives the multivariate discriminant from the database device 400, and (2) the received amino acid concentration data and the multivariate discriminant. (3) By comparing the calculated discriminant value with a preset threshold value, “discriminating whether NASH or non-NASH” or “NAFLD or non-NAFLD” "Determining whether or not there is", "Determining whether or not fatty liver or non-fatty liver", "Determining whether or not NASH or simple fatty liver", or "Non-NAFLD, NASH, or simplicity" “Determining whether or not it is fatty liver” is executed, and (4) the determination result is transmitted to the client device 200 and the database device 400. The client apparatus 200 receives and displays the discrimination result transmitted from the fatty liver disease evaluation apparatus 100, and the database apparatus 400 receives and stores the discrimination result transmitted from the fatty liver disease evaluation apparatus 100. As a result, NASH and non-NASH 2-group discrimination, NAFLD and non-NAFLD 2-group discrimination, fatty liver and non-fatty liver 2-group discrimination, NASH and simple fatty liver 2-group discrimination, or non-NAFLD and NASH and simple Using the discriminant value obtained by the multivariate discriminant useful for the three-group discrimination of fatty fatty liver, the two-group discrimination or the three-group discrimination can be accurately performed.

Here, according to the fatty liver disease evaluation system, the multivariate discriminant used in step SA25 is a logistic regression equation, a fractional equation, a linear discriminant equation, a multiple regression equation, an equation created by a support vector machine, a Mahalanobis distance Any one of an expression created by the method, an expression created by canonical discriminant analysis, and an expression created by a decision tree may be used. As a result, NASH and non-NASH 2-group discrimination, NAFLD and non-NAFLD 2-group discrimination, fatty liver and non-fatty liver 2-group discrimination, NASH and simple fatty liver 2-group discrimination, or non-NAFLD and NASH and simple By using the discriminant value obtained by the multivariate discriminant useful for the 3-group discrimination of the fatty liver, these 2-group discrimination or 3-group discrimination can be performed with higher accuracy.

Specifically, when it is determined whether or not NASH or non-NASH in step SA26, the multivariate discriminant used in step SA25 is a logistic including Glu, Gln, Gly, Ala, Val, and Tyr as variables. A regression equation may be used. Thereby, this two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination of NASH and non-NASH. Further, when determining whether NAFLD or non-NAFLD in step SA26, the multivariate discriminant used in step SA25 is a logistic regression equation including Ser, Glu, Gly, Val, Tyr, and His as variables. Good. This makes it possible to perform the two-group discrimination with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination of NAFLD and non-NAFLD. When determining whether or not the liver is fatty liver or non-fatty liver in step SA26, the multivariate discriminant used in step SA25 is logistic regression including Ser, Glu, Gly, Ala, Val, and Tyr as variables. It may be an expression. Thus, the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between fatty liver and non-fatty liver. Further, when it is determined in step SA26 whether or not it is NASH or simple fatty liver, the multivariate discriminant used in step SA25 is logistic regression including Asn, Gln, Gly, Ala, Cit, and Met as variables. It may be an expression. Thereby, this two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between NASH and simple fatty liver. When determining whether or not non-NAFLD, NASH, or simple fatty liver in step SA26, the multivariate discriminant used in step SA25 is a variable of Ser, Glu, Gly, Val, Tyr, and His. And a logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as variables. Thereby, this three-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the three-group discrimination of non-NAFLD, NASH, and simple fatty liver.

Each multivariate discriminant described above is a method described in International Publication No. 2004/052191 which is an international application by the present applicant or a method described in International Publication No. 2006/098192 which is an international application by the present applicant. It may be created by (multivariate discriminant creation processing described later). If the multivariate discriminant obtained by these methods is used, the multivariate discriminant is preferably used for the evaluation of the state of fatty liver disease regardless of the unit of amino acid concentration in the amino acid concentration data as input data. be able to.

Also, the fatty liver disease evaluation apparatus, fatty liver disease evaluation method, fatty liver disease evaluation program, recording medium, fatty liver disease evaluation system, and information communication terminal device according to the present invention are the second embodiment described above. In addition, the invention may be implemented in various different embodiments within the scope of the technical idea described in the claims. For example, among the processes described in the second embodiment, all or part of the processes described as being automatically performed can be manually performed, or the processes described as being performed manually All or a part of the above can be automatically performed by a known method. In addition, the processing procedures, control procedures, specific names, information including parameters such as various registration data and search conditions, screen examples, and database configurations shown in the above documents and drawings, unless otherwise specified. It can be changed arbitrarily. For example, regarding the fatty liver disease evaluation apparatus 100, each illustrated component is functionally conceptual and does not necessarily need to be physically configured as illustrated. In addition, with regard to the processing functions (particularly the processing functions performed by the control unit 102) of each unit or each device of the fatty liver disease evaluation apparatus 100, a CPU (Central Processing Unit) and a program interpreted and executed by the CPU In this case, all or any part thereof may be realized, or it may be realized as hardware by wired logic. The fatty liver disease evaluation apparatus 100 may be configured as an information processing apparatus such as a known personal computer or workstation, or may be configured by connecting any peripheral device to the information processing apparatus. . The fatty liver disease evaluation apparatus 100 may be realized by installing software (including a program, data, and the like) that causes the information processing apparatus to realize the method of the present invention.

Here, “program” is a data processing method described in an arbitrary language or description method, and may be in any form such as source code or binary code. The “program” is not necessarily limited to a single configuration, but is distributed in the form of a plurality of modules and libraries, or in cooperation with a separate program typified by an OS (Operating System). Includes those that achieve that function. The program is recorded on a recording medium and is mechanically read by the fatty liver disease evaluation apparatus 100 as necessary. That is, in the storage unit 106 such as a ROM or an HDD (Hard Disk Drive), a computer program for giving instructions to the CPU in cooperation with an OS (Operating System) and performing various processes is recorded. This computer program is executed by being loaded into the RAM, and constitutes a control unit in cooperation with the CPU. In addition, this computer program may be stored in an application program server connected to the fatty liver disease evaluation apparatus 100 via a dedicated network 300, and may be downloaded in whole or in part as necessary. It is also possible to do. As a specific configuration for reading the program recorded on the recording medium by each device, a reading procedure, an installation procedure after reading, and the like, a well-known configuration and procedure can be used.

In addition, “recording medium” includes any “portable physical medium”. The “portable physical medium” is a memory card, USB memory, SD card, flexible disk, magneto-optical disk, ROM, EPROM, EEPROM, CD-ROM, MO, DVD, Blu-ray Disc, or the like. . The program according to the present invention may be stored in a computer-readable recording medium, or may be configured as a program product.

Finally, an example of the multivariate discriminant creation process performed by the fatty liver disease evaluation apparatus 100 will be described in detail with reference to FIG. Note that the processing described here is merely an example, and the method of creating the multivariate discriminant is not limited to this. FIG. 22 is a flowchart illustrating an example of multivariate discriminant creation processing. The multivariate discriminant creation process may be performed by the database device 400 that manages fatty liver disease state information.

In this description, the fatty liver disease evaluation device 100 stores the fatty liver disease state information acquired in advance from the database device 400 in a predetermined storage area of the fatty liver disease state information file 106c. And Also, the fatty liver disease evaluation apparatus 100 converts the fatty liver disease state information including the fatty liver disease state index data and the amino acid concentration data designated in advance by the fatty liver disease state information designation unit 102g into the designated fatty liver disease information. It is assumed that it is stored in a predetermined storage area of the disease state information file 106d.

First, the multivariate discriminant-preparing part 102 h is a candidate multivariate discriminant-preparing part 102 h 1 that uses a predetermined formula from the fatty liver disease state information stored in a predetermined storage area of the designated fatty liver disease state information file 106 d. A candidate multivariate discriminant is created based on the creation method, and the created candidate multivariate discriminant is stored in a predetermined storage area of the candidate multivariate discriminant file 106e1 (step SB21). Specifically, first, the multivariate discriminant-preparing part 102h is a candidate multivariate discriminant-preparing part 102h1, and a plurality of different formula creation methods (principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, logistic regression) Analysis, k-means method, cluster analysis, decision tree, etc. related to multivariate analysis.) Select a desired one from among them, and create candidate multivariate discrimination based on the selected formula creation method Determine the form of the expression (form of the expression). Next, the multivariate discriminant-preparing part 102h is a candidate multivariate discriminant-preparing part 102h1, which calculates various (for example, average and variance) corresponding to the formula selection method selected based on the fatty liver disease state information. Execute. Next, the multivariate discriminant-preparing part 102h determines the calculation result and parameters of the determined candidate multivariate discriminant-expression in the candidate multivariate discriminant-preparing part 102h1. Thereby, a candidate multivariate discriminant is created based on the selected formula creation method. In addition, when a candidate multivariate discriminant is created in parallel and in parallel by using a plurality of different formula creation methods, the above-described processing may be executed in parallel for each selected formula creation method. In addition, when a candidate multivariate discriminant is created in series using a plurality of different formula creation methods, for example, a fatty liver disease state using a candidate multivariate discriminant created by performing principal component analysis A candidate multivariate discriminant may be created by converting information and performing discriminant analysis on the converted fatty liver disease state information.

Next, the multivariate discriminant-preparing part 102h verifies (mutually verifies) the candidate multivariate discriminant created in step SB21 with the candidate multivariate discriminant-verifying part 102h2, and verifies the verification result. The result is stored in a predetermined storage area of the verification result file 106e2 (step SB22). Specifically, the multivariate discriminant-preparing part 102h uses the candidate multivariate discriminant-verifying part 102h2 to store the fatty liver disease state information stored in a predetermined storage area of the designated fatty liver disease state information file 106d. Based on this, the verification data used when verifying the candidate multivariate discriminant is created, and the candidate multivariate discriminant is verified based on the created verification data. If a plurality of candidate multivariate discriminants are created in combination with a plurality of different formula creation methods in step SB21, the multivariate discriminant creation unit 102h creates each formula in the candidate multivariate discriminant verification unit 102h2. Each candidate multivariate discriminant corresponding to the method is verified based on a predetermined verification method. Here, in step SB22, the discrimination rate, sensitivity, specificity, information criterion of the candidate multivariate discriminant based on at least one of the bootstrap method, holdout method, N-fold method, leave one out method, etc. , ROC_AUC (area under the curve of the receiver characteristic curve) or the like. Thereby, a candidate index formula having high predictability or robustness in consideration of fatty liver disease state information and diagnosis conditions can be selected.

Next, the multivariate discriminant-preparing part 102h selects the variable of the candidate multivariate discriminant based on a predetermined variable selection method from the verification result in step SB22 by the variable selection part 102h3 (however, the step The variable of the candidate multivariate discriminant may be selected based on a predetermined variable selection method without considering the verification result in SB22.), Fatty liver disease used when creating the candidate multivariate discriminant A combination of amino acid concentration data included in the state information is selected, and fatty liver disease state information including the selected combination of amino acid concentration data is stored in a predetermined storage area of the selected fatty liver disease state information file 106e3 (step SB23). ). In step SB21, a plurality of candidate multivariate discriminants are created in combination with a plurality of different formula creation methods, and in step SB22, each candidate multivariate discriminant corresponding to each formula creation method is verified based on a predetermined verification method In such a case, in step SB23, the multivariate discriminant-preparing part 102h is predetermined for each candidate multivariate discriminant (for example, the candidate multivariate discriminant corresponding to the verification result in step SB22) by the variable selector 102h3. The variable of the candidate multivariate discriminant may be selected based on the variable selection method. Here, in step SB23, the variable of the candidate multivariate discriminant may be selected based on at least one of the stepwise method, the best path method, the neighborhood search method, and the genetic algorithm from the verification result. The best path method is a method of selecting variables by sequentially reducing the variables included in the candidate multivariate discriminant one by one and optimizing the evaluation index given by the candidate multivariate discriminant. In step SB23, the multivariate discriminant-preparing part 102h uses the variable selection part 102h3 to select amino acids based on fatty liver disease state information stored in a predetermined storage area of the designated fatty liver disease state information file 106d. A combination of density data may be selected.

Next, the multivariate discriminant-preparing part 102h completes all combinations of amino acid concentration data included in the fatty liver disease state information stored in the predetermined storage area of the designated fatty liver disease state information file 106d. If the determination result is “end” (step SB24: Yes), the process proceeds to the next step (step SB25). If the determination result is not “end” (step SB24: No) ) Returns to Step SB21. The multivariate discriminant-preparing part 102h determines whether or not the preset number of times has ended, and if the determination result is “end” (step SB24: Yes), the next step (step SB25). If the determination result is not “end” (step SB24: No), the process may return to step SB21. Further, the multivariate discriminant-preparing part 102h includes the combination of the amino acid concentration data selected in step SB23 in the fatty liver disease state information stored in the predetermined storage area of the designated fatty liver disease state information file 106d. Is determined to be the same as the combination of the amino acid concentration data or the combination of the amino acid concentration data selected in the previous step SB23, and if the determination result is “same” (step SB24: Yes) The process proceeds to step (step SB25), and if the determination result is not “same” (step SB24: No), the process may return to step SB21. Further, when the verification result is specifically an evaluation value related to each candidate multivariate discriminant, the multivariate discriminant creation unit 102h compares the evaluation value with a predetermined threshold corresponding to each formula creation method. Based on the result, it may be determined whether to proceed to step SB25 or to return to step SB21.

Next, the multivariate discriminant-preparing part 102h selects a multivariate discriminant by selecting a candidate multivariate discriminant to be adopted as a multivariate discriminant from a plurality of candidate multivariate discriminants based on the verification result. The determined multivariate discriminant (selected candidate multivariate discriminant) is stored in a predetermined storage area of the multivariate discriminant file 106e4 (step SB25). Here, in step SB25, for example, when the optimum one is selected from candidate multivariate discriminants created by the same formula creation method, and when the optimum one is selected from all candidate multivariate discriminants There is.

This completes the explanation of the multivariate discriminant creation process.

[Third Embodiment]
[3-1. Outline of the present invention]
Here, the outline of the method for searching for a substance for preventing / ameliorating fatty liver disease according to the present invention will be described with reference to FIG. FIG. 23 is a principle configuration diagram showing the basic principle of the present invention.

First, a desired substance group composed of one or a plurality of substances is administered to an evaluation target (for example, an individual such as an animal or a human) (step S31). For example, an appropriate combination of existing drugs, amino acids, foods, and supplements that can be administered to humans (for example, various symptoms of fatty liver disease (specifically, at least one of fatty liver, NAFLD, and NASH) Drugs that are known to be effective for improvement (for example, insulin resistance improvers, biguanite drugs, ursodeoxycholic acid, antihyperlipidemic drugs, or antioxidants) and supplements, etc., as appropriate) May be administered by a predetermined administration method (for example, oral administration) at a predetermined frequency and timing (for example, 3 times a day, after meal) over a predetermined period (for example, a range of 1 day to 12 months). Good. Here, the administration method, dose, and dosage form may be appropriately combined depending on the disease state. The dosage form may be determined based on a known technique. The dose is not particularly defined, but may be given, for example, in a form containing 1 ug to 100 g as an active ingredient.

Next, blood is collected from the evaluation target to which the substance group has been administered in step S31 (step S32).

Next, amino acid concentration data relating to the concentration value of amino acids in blood collected in step S32 is acquired (step S33). In step S11, for example, amino acid concentration data measured by a company or the like that performs amino acid concentration measurement may be acquired. Further, from blood collected from an evaluation target (including plasma, serum, etc.), for example, the following Amino acid concentration data may be obtained by measuring amino acid concentration data by a measurement method such as (A) or (B). Here, the unit of amino acid concentration may be obtained by, for example, molar concentration, weight concentration, or by adding / subtracting / subtracting an arbitrary constant to / from these concentrations.
(A) Plasma was separated from blood by centrifuging the collected blood sample. All plasma samples were stored frozen at −80 ° C. until the measurement of amino acid concentration. For amino acid concentration measurement, acetonitrile was added to remove protein, followed by precolumn derivatization using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and liquid chromatography mass spectrometry The amino acid concentration was analyzed by a total (LC-MS) (see International Publication No. 2003/069328 and International Publication No. 2005/116629).
(B) Plasma was separated from blood by centrifuging the collected blood sample. All plasma samples were stored frozen at −80 ° C. until the measurement of amino acid concentration. When measuring the amino acid concentration, sulfosalicylic acid was added to remove the protein, and then the amino acid concentration was analyzed by an amino acid analyzer based on the post-column derivatization method using a ninhydrin reagent.

Next, based on the amino acid concentration data of the evaluation target acquired in step S33, the evaluation target includes at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis) The state of fatty liver disease is evaluated (step S34).

Next, based on the evaluation result in step S34, it is determined whether the substance group administered in step S31 is for preventing fatty liver disease or improving the state of fatty liver disease (step). S35).

If the determination result in step S35 is “prevent or improve”, the substance group administered in step S31 prevents fatty liver disease or improves the state of fatty liver disease To be explored.

As described above, according to the present invention, a desired substance group is administered to an evaluation object, blood is collected from the evaluation object to which the substance group is administered, and amino acid concentration data relating to the concentration value of amino acids in the collected blood is obtained. Based on the obtained amino acid concentration data, the state of fatty liver disease is evaluated for the evaluation target, and based on the evaluation result, the desired substance group prevents fatty liver disease or It is determined whether or not the condition is improved. Thus, fatty liver disease can be prevented or fatty liver disease can be prevented using a method for evaluating fatty liver disease, which can accurately evaluate the state of fatty liver disease using the concentration of amino acids in blood. It is possible to accurately search for a substance that improves the state of.

Here, before executing step S34, data such as missing values and outliers may be removed from the amino acid concentration data. Thereby, the state of fatty liver disease can be more accurately evaluated.

In step S34, among the Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser included in the amino acid concentration data acquired in step S33. The state of NASH may be evaluated for the evaluation target based on at least one concentration value. Thereby, the state of NASH can be accurately evaluated using the concentration of amino acids related to the state of NASH among the concentrations of amino acids in blood. Specifically, at least one concentration value of Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser included in the amino acid concentration data. Based on this, it may be determined whether it is NASH or non-NASH. Thus, the amino acid concentration useful for the two-group discrimination of NASH and non-NASH among the amino acid concentrations in the blood can be used to accurately perform the two-group discrimination.

In step S34, Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser included in the amino acid concentration data acquired in step S33. , Thr, Asn, the state of NAFLD may be evaluated for each evaluation object based on at least one concentration value. Thereby, the state of NAFLD can be accurately evaluated using the concentration of amino acids related to the state of NAFLD among the concentrations of amino acids in blood. Specifically, among Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn included in the amino acid concentration data. Based on at least one concentration value, it may be determined whether the evaluation target is NAFLD or non-NAFLD. Thus, the amino acid concentration useful for the 2-group discrimination between NAFLD and non-NAFLD among the amino acid concentrations in the blood can be used to accurately perform the 2-group discrimination.

In step S34, Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn included in the amino acid concentration data acquired in step S33. The state of fatty liver may be evaluated for each evaluation target based on at least one concentration value. Thereby, the state of fatty liver can be accurately evaluated using the concentration of amino acids related to the state of fatty liver among the concentrations of amino acids in blood. Specifically, at least one concentration of Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn included in the amino acid concentration data. Based on the value, it may be determined whether or not the subject is a fatty liver or non-fatty liver. Thus, the amino acid concentration useful for the 2-group discrimination between fatty liver and non-fatty liver among the amino acid concentrations in the blood can be used to accurately perform the 2-group discrimination.

In step S34, at least one concentration of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA included in the amino acid concentration data acquired in step S33. Based on the value, the state of NASH and NAFLD may be evaluated for each evaluation object. Thereby, the state of NASH and NAFLD can be accurately evaluated using the concentration of amino acids related to the state of NASH and NAFLD among the concentrations of amino acids in blood. Specifically, based on the concentration value of at least one of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA included in the amino acid concentration data. Whether the subject is NASH or non-NASH and NAFLD may be determined. This makes it possible to accurately perform this 2-group discrimination by using the amino acid concentrations useful for 2-group discrimination between NASH and simple fatty liver among the amino acid concentrations in the blood.

In step S34, based on the amino acid concentration data acquired in step S33 and the preset multivariate discriminant including the amino acid concentration as a variable, a discriminant value that is the value of the multivariate discriminant is calculated and calculated. The state of fatty liver disease may be evaluated for the evaluation target based on the discriminant value. Thereby, the state of fatty liver disease can be accurately evaluated using the discriminant value obtained by the multivariate discriminant including the amino acid concentration as a variable.

In step S34, among the Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser included in the amino acid concentration data acquired in step S33. Multivariate discrimination including at least one concentration value and at least one of Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser The discriminant value may be calculated based on the equation, and the state of NASH may be evaluated for each evaluation object based on the calculated discriminant value. Thereby, the NASH state can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the NASH state. Specifically, it may be determined whether the evaluation target is NASH or non-NASH based on the determination value. This makes it possible to accurately perform the two-group discrimination using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between NASH and non-NASH. The multivariate discriminant may be a logistic regression equation including Glu, Gln, Gly, Ala, Val, Tyr as variables. Thereby, this two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination of NASH and non-NASH.

In step S34, Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser included in the amino acid concentration data acquired in step S33. , Thr, Asn, and Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn A discriminant value may be calculated based on a multivariate discriminant including at least one of them as a variable, and the NAFLD state may be evaluated for each evaluation object based on the calculated discriminant value. Thereby, the NAFLD state can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the NAFLD state. Specifically, it may be determined whether the evaluation target is NAFLD or non-NAFLD based on the determination value. This makes it possible to accurately perform the two-group discrimination by using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between NAFLD and non-NAFLD. Note that the multivariate discriminant may be a logistic regression equation including Ser, Glu, Gly, Val, Tyr, and His as variables. This makes it possible to perform the two-group discrimination with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination of NAFLD and non-NAFLD.

In step S34, Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn included in the amino acid concentration data acquired in step S33. And at least one of Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn A discriminant value may be calculated based on the multivariate discriminant included, and the state of fatty liver may be evaluated for each evaluation object based on the calculated discriminant value. Thereby, the state of fatty liver can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the state of fatty liver. Specifically, based on the discriminant value, it may be discriminated whether the evaluation target is fatty liver or non-fatty liver. This makes it possible to accurately perform the two-group discrimination using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between fatty liver and non-fatty liver. The multivariate discriminant may be a logistic regression equation including Ser, Glu, Gly, Ala, Val, and Tyr as variables. Thus, the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between fatty liver and non-fatty liver.

In step S34, at least one concentration of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA included in the amino acid concentration data acquired in step S33. The discriminant value is based on a multivariate discriminant including at least one of a value and Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA as a variable. The state of NASH and NAFLD may be evaluated for each evaluation object based on the calculated discriminant value. Thereby, the state of NASH and NAFLD can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the state of NASH and NAFLD. Specifically, it may be determined whether the evaluation target is NASH or “non-NASH and NAFLD” (simple fatty liver) based on the determination value. This makes it possible to accurately perform this two-group discrimination using a discriminant value obtained by a multivariate discriminant useful for two-group discrimination between NASH and simple fatty liver. Note that the multivariate discriminant may be a logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as variables. Thereby, this two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between NASH and simple fatty liver. Specifically, it may be determined whether the evaluation target is non-NAFLD, NASH, or “non-NASH and NAFLD” based on the determination value. This makes it possible to accurately perform this three-group discrimination by using the discriminant value obtained by the multivariate discriminant useful for the three-group discrimination of non-NAFLD, NASH, and simple fatty liver. The multivariate discriminant may be a logistic regression equation including Ser, Glu, Gly, Val, Tyr, and His as variables, and a logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as variables. Thereby, this three-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the three-group discrimination of non-NAFLD, NASH, and simple fatty liver.

Here, each multivariate discriminant described above is described in the method described in International Publication No. 2004/052191 which is an international application by the present applicant or International Publication No. 2006/098192 which is an international application by the present applicant. It may be created by a method (multivariate discriminant creation process described in the second embodiment described above). If the multivariate discriminant obtained by these methods is used, the multivariate discriminant is suitable for the evaluation of the state of fatty liver disease regardless of the unit of amino acid concentration in the amino acid concentration data as input data. Can be used.

[3-2. Example of a method for searching for a substance for preventing / ameliorating fatty liver disease according to the third embodiment]
Here, an example of a method for searching for a substance for preventing / ameliorating fatty liver disease according to the third embodiment will be described with reference to FIG. FIG. 24 is a flowchart showing an example of a method for searching for a substance for preventing / ameliorating fatty liver disease according to the third embodiment.

First, a desired substance group composed of one or a plurality of substances is administered to an individual such as an animal or a human having a fatty liver disease (step SA31).

Next, blood is collected from the individual to which the substance group has been administered in step S31 (step SA32).

Next, amino acid concentration data relating to the concentration value of amino acids in blood collected in step S32 is acquired (step SA33). In step SA33, for example, amino acid concentration data measured by a company or the like that measures amino acid concentration may be acquired, and measurement such as (A) or (B) described above is performed from blood collected from an evaluation target. Amino acid concentration data may be obtained by measuring amino acid concentration data by a method.

Next, data such as missing values and outliers are removed from the amino acid concentration data of the individual obtained in step S33 (step SA34).

Next, based on the amino acid concentration data of individuals from which data such as missing values and outliers have been removed in step S34, the following 31. To 35. One of the determinations is executed (step SA35).

31. Discrimination between NASH and non-NASH (i) At least one of Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser included in amino acid concentration data Determine whether each individual is NASH or non-NASH by comparing one concentration value with a preset threshold (cutoff value), or (ii) Gln, included in the amino acid concentration data Concentration value of at least one of Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser, and Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser On the basis of a multivariate discriminant including at least one of the variables, a discriminant value is calculated, and the calculated discriminant value is compared with a preset threshold value (cutoff value). It is determined whether or not it is NASH.

32. Discrimination between NAFLD and non-NAFLD (i) Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr included in amino acid concentration data , Asn to determine whether each individual is NAFLD or non-NAFLD by comparing at least one concentration value with a preset threshold (cutoff value), or (ii) amino acid concentration At least one concentration value of Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn included in the data, and Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, T A discriminant value is calculated based on a multivariate discriminant including at least one of yr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn as a variable, and the calculated discriminant value is set in advance. It is determined whether each individual is NAFLD or non-NAFLD by comparing with the threshold value (cutoff value).

33. Discrimination between fatty liver and non-fatty liver (i) Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn included in amino acid concentration data By comparing at least one concentration value and a preset threshold value (cut-off value), it is determined whether the individual has fatty liver or non-fatty liver, or (ii) amino acid concentration Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn included in the data, and Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, A By calculating a discriminant value based on a multivariate discriminant including at least one of sn and Orn as a variable, and comparing the calculated discriminant value with a preset threshold value (cutoff value), Determine whether it is fatty liver or non-fatty liver.

34. Discrimination between NASH and simple fatty liver (i) Concentration of at least one of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, ABA included in amino acid concentration data By comparing the value with a preset threshold value (cutoff value), it is determined whether the individual has NASH or simple fatty liver (non-NASH and NAFLD), or (ii) amino acid concentration Concentration value of at least one of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, ABA, and Gln, Glu, Gly, Ala, Cit, Atn one of Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, ABA Is calculated on the basis of a multivariate discriminant that includes a variable, and the calculated discriminant value is compared with a preset threshold value (cut-off value), whereby NASH or simple fatty liver ( It is determined whether it is non-NASH and NAFLD.

35. Discrimination between NASH, simple fatty liver and non-NAFLD Concentration of at least one of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro and ABA contained in amino acid concentration data The discriminant value is based on a multivariate discriminant including at least one of a value and Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA as a variable. By calculating and comparing the calculated discriminant value with a preset threshold value (cut-off value), it is determined whether or not each individual is non-NAFLD, NASH, or simple fatty liver (non-NASH and NAFLD). Determine.

Next, based on the determination result in step SA35, it is determined whether the substance group administered in step SA31 is for preventing fatty liver disease or improving the state of fatty liver disease (step). SA36).

If the determination result in step SA36 is “prevent or improve”, the substance group administered in step SA31 is searched for preventing fatty liver disease or improving the state of fatty liver disease. Is done. In addition, as a substance searched by this search method, for example, at least 1 of “Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser” A group of amino acids containing at least one of Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, Thr, Asn. Amino acid group containing at least one of Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn " , And "Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Or , Phe, Met, Ile, Pro, a group of amino acids comprising at least one of ABA "are mentioned.

[3-3. Summary of Third Embodiment and Other Embodiments]
As described above in detail, according to the method for searching for a substance for preventing / ameliorating fatty liver disease according to the third embodiment, (i) a desired substance group is administered to an individual; (ii) Blood is collected from the administered individual, (iii) amino acid concentration data in the collected blood is obtained, (iv) data such as missing values and outliers are removed from the obtained amino acid concentration data of the individual, (v ) Based on the amino acid concentration data of individuals from which data such as missing values and outliers have been removed, the above-mentioned 31. 35. (Vi) whether the administered substance group prevents fatty liver disease or improves the state of fatty liver disease based on the result of this discrimination Is determined. Thereby, it is possible to accurately search for a substance that prevents fatty liver disease or improves the state of fatty liver disease using the method for evaluating fatty liver disease of the first embodiment described above.

Here, the multivariate discriminant used in step SA35 is a logistic regression equation, a fractional equation, a linear discriminant equation, a multiple regression equation, an equation created by a support vector machine, an equation created by the Mahalanobis distance method, and a canonical discriminant. Any one of an expression created by analysis and an expression created by a decision tree may be used. As a result, NASH and non-NASH 2-group discrimination, NAFLD and non-NAFLD 2-group discrimination, fatty liver and non-fatty liver 2-group discrimination, NASH and simple fatty liver 2-group discrimination, or non-NAFLD and NASH and simple By using the discriminant value obtained by the multivariate discriminant useful for the 3-group discrimination of the fatty liver, these 2-group discrimination or 3-group discrimination can be performed with higher accuracy.

Specifically, 31. The multivariate discriminant used in the discriminant may be a logistic regression equation including Glu, Gln, Gly, Ala, Val, Tyr as variables. Thereby, this two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination of NASH and non-NASH. In addition, the 32. mentioned above. The multivariate discriminant used in discriminating the above may be a logistic regression equation including Ser, Glu, Gly, Val, Tyr, and His as variables. This makes it possible to perform the two-group discrimination with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination of NAFLD and non-NAFLD. In addition, as described above 33. The multivariate discriminant used in this discrimination may be a logistic regression equation including Ser, Glu, Gly, Ala, Val, and Tyr as variables. Thus, the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between fatty liver and non-fatty liver. In addition, as described above 34. The multivariate discriminant used in the discriminant may be a logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as variables. Thereby, this two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between NASH and simple fatty liver. In addition, as described above 35. The multivariate discriminant used for discriminating is a logistic regression equation including Ser, Glu, Gly, Val, Tyr, and His as variables, and a logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as variables. Good. Thereby, this three-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the three-group discrimination of non-NAFLD, NASH, and simple fatty liver.

Each multivariate discriminant described above is a method described in International Publication No. 2004/052191 which is an international application by the present applicant or a method described in International Publication No. 2006/098192 which is an international application by the present applicant. It may be created by (multivariate discriminant creation processing described in the second embodiment described above). If the multivariate discriminant obtained by these methods is used, the multivariate discriminant is suitable for the evaluation of the state of fatty liver disease regardless of the unit of amino acid concentration in the amino acid concentration data as input data. Can be used.

The method for searching for a substance for preventing or improving fatty liver disease according to the third embodiment is described in “Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr. , Asn, Ser, a group of amino acids containing at least one of “Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser, An amino acid group containing at least one of Thr and Asn ”,“ Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn Amino acid group containing at least one ", or" Gln, Glu, Gly, Ala, Cit, Asn, Tr " , Leu, Orn, Phe, Met, Ile, Pro, and ABA, a substance that normalizes the concentration value of each of the multivariate discriminants described above, It can be selected by using the form of the fatty liver disease evaluation method or the fatty liver disease evaluation apparatus of the second embodiment.

In the method for searching for a substance for preventing / ameliorating fatty liver disease according to the third embodiment, “searching for a substance for preventing / ameliorating” means finding a new substance effective for the prevention / amelioration of fatty liver disease. In addition to finding new uses of known substances for preventing and improving fatty liver disease, and combining new drugs and supplements that can be expected to be effective in preventing and improving fatty liver disease. Finding and finding the appropriate usage / dose / combination as described above, making it a kit, presenting a prevention / treatment menu including food / exercise, etc., and monitoring the effectiveness of the prevention / treatment menu And presenting menu changes for each individual as needed.

Based on the diagnosis results, the patients who had undergone ultrasound diagnosis regarding the presence or absence of fatty liver were classified into two groups: fatty liver negative and fatty liver positive. There were 561 people. The amino acid concentration in plasma collected from the examinee was measured, and the ability to discriminate fatty liver positive for each amino acid concentration was evaluated by ROC_AUC (area under the curve of the receiver characteristic curve). The amino acid concentration was measured by the measurement method (A) described in the above embodiment.

Amino acids that were significant (p <0.05) in the test when ROC_AUC is nonparametric and the null hypothesis is ROC_AUC = 0.5 are Thr, Ser, Glu, Pro, Gly, Ala , Cit, Leu, Ile, Val, Tyr, Phe, Met, His, and Trp. Among these amino acids, Glu, Pro, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, and Trp showed a significant increase in the fatty liver positive group, while Thr, Ser, Gly, and Cit. Showed a significant decrease in the fatty liver positive group.

In the diagnosis of NAFLD (non-alcoholic fatty liver disease), since the examinees who satisfy the following four diagnostic conditions are considered to be NAFLD high risk groups, the examinees were classified into NAFLD positive groups.
Diagnosis condition 1) A diagnosis result that there was fatty liver was obtained by ultrasonic diagnosis.
Diagnosis condition 2) The ALT value is high (38 (IU / L) or more).
Diagnosis condition 3) There is no large intake of alcohol (daily intake). (Exclusion rules)
Diagnosis condition 4) Not positive for hepatitis virus HBV and HCV. (Exclusion rules)

The subjects were classified into two groups, NAFLD-negative and NAFLD-positive, based on these four diagnostic conditions. The NAFLD-negative and NAFLD-positive groups were 1415 and 167, respectively. The amino acid concentration in plasma collected from the examinee was measured, and the NAFLD positive discrimination ability for each amino acid concentration was evaluated by ROC_AUC. The amino acid concentration was measured by the measurement method (A) described in the above embodiment.

The amino acids that were significant (p <0.05) in the test when ROC_AUC is nonparametric and the null hypothesis is ROC_AUC = 0.5 are Gln, Glu, Pro, Gly, Ala, Cit Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys. Among these amino acids, Glu, Pro, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, and Lys show a significant increase in the NAFLD positive group, while Gln, Gly, and Cit The NAFLD positive group showed a significant decrease.

In the diagnosis of NASH (non-alcoholic steatohepatitis), patients who meet the five diagnostic conditions obtained by adding the following diagnostic conditions 5) to the NAFLD diagnostic conditions 1) to 4) shown in Example 2 are NASH high risk groups Therefore, the examinee was classified into a NASH positive group.
Diagnosis condition 5) Satisfy the diagnosis condition of metabolic syndrome (refer to the document “Metabolic Syndrome Diagnosis Criteria Review Committee, Journal of Japan Society for Internal Medicine, 94, 794, 2005”).

The examinees were classified into two groups, NASH negative and NASH positive, based on these five diagnostic conditions, and the NASH negative group and NASH positive group were 1518 and 64, respectively. The amino acid concentration in plasma collected from the examinee was measured, and the NASH positive discrimination ability for each amino acid concentration was evaluated by ROC_AUC. The amino acid concentration was measured by the measurement method (A) described in the above embodiment.

Amino acids that were significant (p <0.05) in the test when ROC_AUC is nonparametric and the null hypothesis is ROC_AUC = 0.5 are Gln, Glu, Pro, Gly, Ala, Leu Ile, Val, Tyr, Phe, Met, His, Trp. Among these amino acids, Glu, Pro, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, and Trp show a significant increase in the NASH positive group, while Gln and Gly have the NASH positive group. Showed a significant decrease.

Using the same amino acid concentration data as measured in Example 1, it is effective for the diagnosis of fatty liver described in Example 1, and multivariate discrimination for discriminating positive fatty liver having amino acid concentration in plasma as a variable. The formula (multivariate function) was obtained.

First, the logistic regression equation is used as a multivariate discriminant, the combination of variables included in the logistic regression equation is searched, and the Leave-One-Out method is adopted as a cross-validation. The search of the logistic regression equation was carried out earnestly.

A list of logistic regression equations with equally good discrimination ability evaluated by ROC_AUC is shown in FIG. 25 and FIG. Here, FIGS. 25 and 26 show combinations of variables included in the logistic regression equation, ROC_AUC values with cross validation, and ROC_AUC values without cross validation. Enumerated in descending order of the appearance frequency of variables in the expressions included in FIGS. 25 and 26 are Glu, Ala, Tyr, Ser, Gly, Val, Leu, Ile, Cit, and His.

Among logistic regression equations with equally good discriminating ability, for example, an index formula “(−4.833) + (− 0.017) having a variable set“ Ser, Glu, Gly, Ala, Val, Tyr ”. ) Ser + (0.0344) Glu + (− 0.0057) Gly + (0.0049) Ala + (0.00675) Val + (0.025) Tyr ”, ROC_AUC = 0.996, sensitivity = 0.715 , Specificity was 0.731 and good.

Also, using fractional expressions as multivariate discriminants, searching for combinations of variables to be included in fractional expressions, and adopting the bootstrap method as cross-validation, search for fractional expressions with good discrimination ability of fatty liver positive. Conducted earnestly.

FIG. 27 and FIG. 28 show a list of fractional expressions with equally good discrimination ability evaluated by ROC_AUC. Here, FIG. 27 and FIG. 28 show fractional expressions, average values of ROC_AUC values with cross validation, and ROC_AUC values without cross validation. Enumerated in descending order of the appearance frequency of variables in the expressions included in FIGS. 27 and 28 are Glu, Gly, Ser, Tyr, Cit, Ala, Asn, Orn, Ile, and Met.

Using the same amino acid concentration data as measured in Example 2, a multivariate discriminant for determining NAFLD positivity having the amino acid concentration in plasma as a variable, which is effective for the diagnosis of NAFLD described in Example 2 ( Multivariate function).

First, use logistic regression as a multivariate discriminant, search for combinations of variables to be included in the logistic regression, and adopt the Leave-One-Out method as cross-validation, and logistic with good NAFLD positive discrimination The search of the regression equation was conducted earnestly.

29 and 30 show a list of logistic regression equations with equally good discrimination ability evaluated by ROC_AUC. Here, FIGS. 29 and 30 show combinations of variables included in the logistic regression equation, ROC_AUC values with cross-validation, and ROC_AUC values without cross-validation. Enumerating the appearance frequency of variables in the formulas included in FIG. 29 and FIG. 30 up to the 10th order is Glu, Tyr, His, Val, Orn, Ile, Ser, Thr, Trp, Phe.

Among logistic regression equations having equally good discriminating ability, for example, an index formula “(−9.035) + (− 0.0121) having a variable set“ Ser, Glu, Gly, Val, Tyr, His ”is used. ) Ser + (0.0325) Glu + (− 0.00565) Gly + (0.0113) Val + (0.0299) Tyr + (0.0271) His ”has a discriminating ability of ROC_AUC = 0.825, sensitivity = 0.737. The specificity was 0.776 and good.

Also, using fractional expressions as multivariate discriminants, searching for combinations of variables to be included in fractional expressions, and employing the bootstrap method as cross-validation, we are eager to search for fractional expressions with good NAFLD positive discriminating ability. Carried out.

A list of fractional expressions with equally good discrimination ability evaluated by ROC_AUC is shown in FIG. 31 and FIG. Here, FIG. 31 and FIG. 32 show fractional expressions, average values of ROC_AUC values with cross validation, and ROC_AUC values without cross validation. When the appearance frequency of the variables in the formulas included in FIGS. 31 and 32 is listed in descending order, they are Glu, Tyr, Gly, Cit, Orn, Ser, Asn, His, Met, and Ile.

A multivariate discriminant for determining NASH positivity having the amino acid concentration in plasma as a variable, which is effective for diagnosis of NASH described in Example 3, using the same amino acid concentration data as measured in Example 3 ( Multivariate function).

First, use logistic regression as a multivariate discriminant, search for combinations of variables to be included in the logistic regression, and adopt the Leave-One-Out method as cross-validation, and logistic with good NASH positive discrimination The search of the regression equation was conducted earnestly.

33 and 34 show a list of multivariate logistic regression equations with equally good discrimination ability evaluated by ROC_AUC. Here, FIGS. 33 and 34 show combinations of variables included in the logistic regression equation, ROC_AUC values with cross validation, and ROC_AUC values without cross validation. When the appearance frequency of the variables in the formulas included in FIGS. 33 and 34 is listed up to 10th in descending order, they are Glu, Tyr, Ala, Val, Gln, His, Phe, Thr, Asn, Ser.

Among logistic regression equations with equally good discriminability, for example, an index formula “(−7.443) + (0.0283) having a variable set“ Glu, Gln, Gly, Ala, Val, Tyr ”. The discriminating ability of “Glu + (− 0.00648) Gln + (− 0.00757) Gly + (0.00468) Ala + (0.0131) Val + (0.0298) Tyr” is ROC_AUC = 0.857, sensitivity = 0.766 , Specificity was 0.789 and good.

Also, using fractional expressions as multivariate discriminants, searching for combinations of variables to be included in fractional expressions, and using the bootstrap method as cross-validation, eager to search for fractional expressions with good NASH positive discriminating ability Carried out.

FIG. 35 and FIG. 36 show a list of fractional expressions with equally good discriminating ability evaluated by ROC_AUC. Here, FIG. 35 and FIG. 36 show fractional expressions, average values of ROC_AUC values with cross validation, and ROC_AUC values without cross validation. When the variable frequencies in the expressions included in FIGS. 35 and 36 are listed in descending order of the frequency of occurrence, they are Glu, Gly, Gln, Ala, Tyr, Val, His, Ser, Met, Thr.

Based on the diagnosis of NAFLD described in Example 2 and the diagnosis of NASH described in Example 3, NAFLD-positive patients were classified into two groups, simple fatty liver (simple steatosis) and NASH-positive. The fatty liver group and NASH positive group were 103 and 64, respectively. The amino acid concentration in plasma collected from NAFLD-positive examinees was measured, and the NASH-positive discrimination ability for each amino acid concentration was evaluated by ROC_AUC. The amino acid concentration was measured by the measurement method (A) described in the above embodiment.

The amino acids that were significant (p <0.05) in the test when ROC_AUC was nonparametric and the null hypothesis was ROC_AUC = 0.5 were Gln, Glu, Gly, and Ala. Among these amino acids, Glu and Ala showed a significant increase in the NASH positive group, while Gln and Gly showed a significant decrease in the NASH positive group.

Also, using the same amino acid concentration data as measured in this example, a multivariate discriminant (multivariate function) for determining NASH positivity having the amino acid concentration in plasma as a variable was obtained.

First, logistic regression using logistic regression as a multivariate function, searching for combinations of variables to be included in logistic regression, and adopting Leave-One-Out method as cross-validation, logistic regression with good NASH positive discrimination The search of the expression was carried out earnestly.

FIG. 37 and FIG. 38 show a list of logistic regression equations with equally good discrimination ability evaluated by ROC_AUC. Here, FIGS. 37 and 38 show combinations of variables included in the logistic regression equation, ROC_AUC values with cross validation, and ROC_AUC values without cross validation. When the appearance frequency of the variables in the formulas included in FIGS. 37 and 38 is listed in descending order, they are Ala, Cit, Gln, Asn, Trp, Leu, Orn, Phe, Met, and Ile.

Among logistic regression equations with equally good discriminating ability, for example, an index formula “(1.989) + (− 0.0708) having a variable set“ Asn, Gln, Gly, Ala, Cit, Met ”. Asn + (− 0.0104) Gln + (− 0.00473) Gly + (0.00649) Ala + (0.0776) Cit + (0.0768) Met ”has a discriminating ability of ROC_AUC = 0.553, sensitivity = 0.688. Specificity = 0.718 was good.

39 and 40 show a list of fractional expressions with equally good discriminating ability evaluated by ROC_AUC. Here, FIG. 39 and FIG. 40 show fractional expressions, average values of ROC_AUC values with cross validation, and ROC_AUC values without cross validation. 39. When the appearance frequency of the variables in the formulas included in FIGS. 39 and 40 is listed up to 10th in descending order, they are Cit, Gln, Ala, Asn, Leu, Pro, Trp, Met, Glu, and ABA.

Based on the diagnosis of NAFLD described in Example 2 and the diagnosis of NASH described in Example 3, all examinees are classified into three groups: NAFLD negative (normal), simple fatty liver (simple steatosis), and NASH positive. As a result, normal, simple fatty liver, and NASH positive were 1415, 103, and 64, respectively. The concentration of amino acids in plasma collected from the examinee is measured, and a multivariate function expression including each amino acid as a variable is first used to determine whether all examinees are normal or NAFLD positive, and then NAFLD By discriminating between simple fatty liver and NASH positivity for the group determined to be positive, two groups of normal, simple fatty liver and NASH positive were discriminated as a whole. The amino acid concentration was measured by the measurement method (A) described in the above embodiment. In the first discrimination between normal and NAFLD positive, the multivariate function expression “(−9.035) + (− 0.0121) Ser + (0.0325) Glu + (− 0.00565) Gly + (0 .0113) Val + (0.0299) Tyr + (0.0271) His ”and the discrimination between simple fatty liver and NASH positive for the group determined to be NAFLD positive next, the multivariate function described in Example 7 Using the formula “(1.989) + (− 0.0708) Asn + (− 0.0104) Gln + (− 0.00473) Gly + (0.00649) Ala + (0.0776) Cit + (0.0768) Met” It was.

FIG. 41 shows the results of discrimination between three groups of normal, simple fatty liver and NASH positive in two stages (in the figure, normal is represented as Normal and simple fatty liver is represented as Steatosis). The four sets of numbers in the figure represent the total number of each discrimination prediction result (number of normal, simple fatty liver, NASH positive 3 groups).

Prevalence (Prev), sensitivity (Sen), positive predictive value (PPV), and predictive enrichment rate (PPV / Prev) for the results of discrimination of 3 groups of 2 stages normal, simple fatty liver, NASH positive Is shown in FIG. 42 (in the figure, normal is expressed as Normal and simple fatty liver is expressed as Steatosis).

In the discrimination between the three groups of normal, simple fatty liver and NASH positive in two stages, the concentration rate of NASH positive prediction was about 5 times, which was a good discrimination ability.

As described above, the method for evaluating fatty liver disease according to the present invention can be widely implemented in many industrial fields, particularly pharmaceuticals, foods, and medical fields. It is extremely useful in the field of bioinformatics that performs state progression prediction, disease risk prediction, proteome and metabolomic analysis.

DESCRIPTION OF SYMBOLS 100 Fatty liver disease evaluation apparatus 102 Control part 102a Request interpretation part 102b Browsing process part 102c Authentication process part 102d E-mail production | generation part 102e Web page production | generation part 102f Reception part 102g Fatty liver disease state information designation | designated part 102h Multivariate discriminant preparation Unit 102h1 candidate multivariate discriminant creation unit 102h2 candidate multivariate discriminant verification unit 102h3 variable selection unit 102i discriminant value calculation unit 102j discriminant value criterion evaluation unit 102j1 discriminant value criterion discriminator 102k result output unit 102m transmission unit 104 communication interface unit 106 Storage unit 106a User information file 106b Amino acid concentration data file 106c Fatty liver disease state information file 106d Designated fatty liver disease state information file 106e Multivariate discriminant-related information database 06e1 candidate multivariate discriminant file 106e2 verification result file 106e3 selected fatty liver disease state information file 106e4 multivariate discriminant file 106f discriminant value file 106g evaluation result file 108 input / output interface unit 112 input device 114 output device 200 client device (information Communication terminal device)
300 network 400 database device

Claims

An acquisition step of acquiring amino acid concentration data relating to the concentration value of amino acids in blood collected from the evaluation target;
Based on the amino acid concentration data of the evaluation target acquired in the acquisition step, at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcohol steathepatitis) is determined for the evaluation target. A method for evaluating fatty liver disease, comprising: a concentration value reference evaluation step for evaluating a state of fatty liver disease.
The concentration value reference evaluation step includes Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn included in the amino acid concentration data acquired in the acquisition step. , Evaluating the state of the NASH for the evaluation object based on the concentration value of at least one of Ser,
The method for evaluating fatty liver disease according to claim 1.
The concentration value reference evaluation step includes Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn included in the amino acid concentration data acquired in the acquisition step. 3. The fatty liver disease according to claim 2, further comprising: a concentration value criterion determination step for determining whether the NASH or the non-NASH is based on the concentration value of at least one of Ser and Ser. Evaluation method.
The concentration value reference evaluation step includes Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys included in the amino acid concentration data acquired in the acquisition step. , Orn, Ser, Thr, Asn, based on the concentration value, evaluating the state of the NAFLD for the evaluation object,
The method for evaluating fatty liver disease according to claim 1.
The concentration value reference evaluation step includes Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys included in the amino acid concentration data acquired in the acquisition step. , Orn, Ser, Thr, Asn, and further comprising a concentration value reference determining step for determining whether the evaluation object is the NAFLD or the non-NAFLD based on the concentration value. The method for evaluating fatty liver disease according to claim 4.
The concentration value reference evaluation step includes Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp included in the amino acid concentration data acquired in the acquisition step. , Asn, Orn, based on the concentration value of at least one of the evaluation object, to evaluate the state of the fatty liver,
The method for evaluating fatty liver disease according to claim 1.
The concentration value reference evaluation step includes Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp included in the amino acid concentration data acquired in the acquisition step. , Asn, Orn, further comprising: a concentration value reference determining step for determining whether the evaluation target is the fatty liver or non-fatty liver based on the concentration value. Item 7. The method for evaluating fatty liver disease according to Item 6.
The concentration value reference evaluation step includes Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA included in the amino acid concentration data acquired in the acquisition step. Evaluating the state of the NASH and the NAFLD for the evaluation object based on at least one of the concentration values;
The method for evaluating fatty liver disease according to claim 1.
The concentration value reference evaluation step includes Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA included in the amino acid concentration data acquired in the acquisition step. The density value reference determination step of determining whether the evaluation target is the NASH or the non-NASH and the NAFLD based on at least one of the density values. Method for fatty liver disease in children.
The density value reference evaluation step includes:
A discriminant value calculating step of calculating a discriminant value which is a value of the multivariate discriminant based on the amino acid concentration data acquired in the acquiring step and a preset multivariate discriminant including the amino acid concentration as a variable; ,
A discriminant value criterion evaluating step for evaluating the state of fatty liver disease for the evaluation object based on the discriminant value calculated in the discriminant value calculating step;
The method for evaluating fatty liver disease according to claim 1.
The multivariate discriminant is a logistic regression formula, fractional formula, linear discriminant formula, multiple regression formula, formula created with support vector machine, formula created with Mahalanobis distance method, formula created with canonical discriminant analysis, Be one of the formulas created in the decision tree,
The method for evaluating fatty liver disease according to claim 10.
The discriminant value calculating step includes Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, included in the amino acid concentration data acquired in the acquiring step. And at least one of the concentration values of Ser, and at least one of Gln, Glu, Pro, Gly, Ala, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Thr, Asn, Ser. Calculating the discriminant value based on the multivariate discriminant included as:
The discriminant value reference evaluation step evaluates the state of the NASH for the evaluation object based on the discriminant value calculated in the discriminant value calculation step;
The method for evaluating fatty liver disease according to claim 10 or 11.
The discriminant value criterion evaluation step further includes a discriminant value criterion discriminating step for discriminating whether the evaluation target is the NASH or non-NASH based on the discriminant value calculated in the discriminant value calculation step. The method for evaluating fatty liver disease according to claim 12.
The multivariate discriminant is the logistic regression equation including Glu, Gln, Gly, Ala, Val, Tyr as the variables;
The method for evaluating fatty liver disease according to claim 13.
The discriminant value calculating step includes Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, included in the amino acid concentration data acquired in the acquiring step. The concentration value of at least one of Orn, Ser, Thr, Asn, and Gln, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Lys, Orn, Ser , Thr, Asn based on the multivariate discriminant including at least one of the variables as the variable,
The discriminant value reference evaluation step evaluates the state of the NAFLD for the evaluation object based on the discriminant value calculated in the discriminant value calculation step;
The method for evaluating fatty liver disease according to claim 10 or 11.
The discriminant value criterion evaluation step further includes a discriminant value criterion discriminating step for discriminating whether the evaluation object is the NAFLD or non-NAFLD based on the discriminant value calculated in the discriminant value calculation step. The method for evaluating fatty liver disease according to claim 15.
The multivariate discriminant is the logistic regression equation including Ser, Glu, Gly, Val, Tyr, His as the variables;
The method for evaluating fatty liver disease according to claim 16.
The discriminant value calculating step includes Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, included in the amino acid concentration data acquired in the acquiring step. At least one of Asn and Orn, and at least one of Thr, Ser, Glu, Pro, Gly, Ala, Cit, Leu, Ile, Val, Tyr, Phe, Met, His, Trp, Asn, Orn Calculating the discriminant value based on the multivariate discriminant including one as the variable,
The discriminant value criterion evaluation step evaluates the state of fatty liver for the evaluation object based on the discriminant value calculated in the discriminant value calculation step;
The method for evaluating fatty liver disease according to claim 10 or 11.
The discriminant value criterion evaluation step further includes a discriminant value criterion discriminating step for discriminating whether the evaluation target is the fatty liver or non-fatty liver based on the discriminant value calculated in the discriminant value calculating step. The method for evaluating fatty liver disease according to claim 18, comprising:
The multivariate discriminant is the logistic regression equation including Ser, Glu, Gly, Ala, Val, Tyr as the variables;
The method for evaluating fatty liver disease according to claim 19.
The discriminant value calculating step includes at least one of Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA included in the amino acid concentration data acquired in the acquiring step. The multivariate discriminant including at least one of the one concentration value and Gln, Glu, Gly, Ala, Cit, Asn, Trp, Leu, Orn, Phe, Met, Ile, Pro, and ABA as the variable. Based on the discriminant value,
The discriminant value reference evaluation step evaluates the state of the NASH and the NAFLD for the evaluation object based on the discriminant value calculated in the discriminant value calculation step;
The method for evaluating fatty liver disease according to claim 10 or 11.
In the discriminant value criterion evaluation step, a discriminant value criterion discriminating step for discriminating whether the evaluation target is the NASH, non-NASH, or the NAFLD based on the discriminant value calculated in the discriminant value calculation step. The method for evaluating fatty liver disease according to claim 21, further comprising:
The multivariate discriminant is the logistic regression equation including Asn, Gln, Gly, Ala, Cit, Met as the variables;
The method for evaluating fatty liver disease according to claim 22.
The discriminant value reference evaluation step discriminates whether or not the evaluation object is non-NAFLD, NASH, non-NASH and NAFLD based on the discriminant value calculated in the discriminant value calculation step. The method for evaluating fatty liver disease according to claim 21, further comprising a reference discriminating step.
The multivariate discriminant is the logistic regression equation including Ser, Glu, Gly, Val, Tyr, and His as the variables, and the logistic regression equation including Asn, Gln, Gly, Ala, Cit, and Met as the variables. There is,
The method for evaluating fatty liver disease according to claim 24.
A control means and a storage means are provided, and the status of fatty liver disease including at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis) is evaluated for an evaluation target An apparatus for evaluating fatty liver disease,
The control means includes
A discriminant value that is a value of the multivariate discriminant based on the previously obtained amino acid concentration data of the evaluation object relating to the amino acid concentration value and the multivariate discriminant stored in the storage means including the amino acid concentration as a variable Discriminant value calculating means for calculating
Based on the discriminant value calculated by the discriminant value calculating unit, the discriminant value criterion-evaluating unit for evaluating the status of the fatty liver disease for the evaluation target,
An apparatus for evaluating fatty liver disease.
A fat that includes at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis), which is executed in an information processing apparatus including a control unit and a storage unit A method for evaluating fatty liver disease for evaluating the state of congenital liver disease,
Executed in the control means,
A discriminant value that is a value of the multivariate discriminant based on the previously obtained amino acid concentration data of the evaluation object relating to the amino acid concentration value and the multivariate discriminant stored in the storage means including the amino acid concentration as a variable A discriminant value calculating step for calculating
A discriminant value criterion evaluating step for evaluating the state of fatty liver disease for the evaluation object based on the discriminant value calculated in the discriminant value calculating step;
A method for evaluating fatty liver disease.
Evaluation targets to be executed in an information processing apparatus including a control unit and a storage unit include at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcohol steathepatitis) A fatty liver disease evaluation program for evaluating the status of fatty liver disease,
For execution in the control means;
A discriminant value that is a value of the multivariate discriminant based on the previously obtained amino acid concentration data of the evaluation object relating to the amino acid concentration value and the multivariate discriminant stored in the storage means including the amino acid concentration as a variable A discriminant value calculating step for calculating
A discriminant value criterion evaluating step for evaluating the state of fatty liver disease for the evaluation object based on the discriminant value calculated in the discriminant value calculating step;
An evaluation program for fatty liver disease characterized by
A control means and a storage means are provided, and the status of fatty liver disease including at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis) is evaluated for an evaluation target Fatty liver disease evaluation apparatus, comprising a control means, and an information communication terminal apparatus that provides amino acid concentration data of the evaluation target relating to the amino acid concentration value, and configured to be communicably connected via a network A liver disease evaluation system,
The control means of the information communication terminal device comprises:
Amino acid concentration data transmitting means for transmitting the evaluation target amino acid concentration data to the fatty liver disease evaluation device;
An evaluation result receiving means for receiving the evaluation result of the evaluation object related to the state evaluation of the fatty liver disease transmitted from the fatty liver disease evaluation device, and
The control means of the fatty liver disease evaluation apparatus comprises:
Amino acid concentration data receiving means for receiving the amino acid concentration data transmitted from the information communication terminal device;
Based on the amino acid concentration data received by the amino acid concentration data receiving means and the multivariate discriminant stored in the storage means including the amino acid concentration as a variable, a discriminant value that is the value of the multivariate discriminant is calculated. Discriminant value calculating means for
Based on the discriminant value calculated by the discriminant value calculating unit, the discriminant value criterion-evaluating unit that evaluates the state of the fatty liver disease for the evaluation target;
Evaluation result transmission means for transmitting the evaluation result of the evaluation object in the discriminant value reference evaluation means to the information communication terminal device;
Having
Fatty liver disease evaluation system characterized by
A fatty liver disease evaluation apparatus and network for evaluating the state of fatty liver disease including at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcohol steatohepatitis) An information communication terminal device comprising control means connected to be communicable via the communication device, and providing the amino acid concentration data of the evaluation object related to the amino acid concentration value,
The control means includes
Amino acid concentration data transmitting means for transmitting the evaluation target amino acid concentration data to the fatty liver disease evaluation device;
An evaluation result receiving means for receiving the evaluation result of the evaluation object related to the state evaluation of the fatty liver disease transmitted from the fatty liver disease evaluation device, and
The evaluation result indicates that the apparatus for evaluating fatty liver disease receives the amino acid concentration data transmitted from the information communication terminal device, and includes the received amino acid concentration data and the amino acid concentration as variables. Based on the multivariate discriminant stored in the liver disease evaluation apparatus, a discriminant value that is the value of the multivariate discriminant is calculated, and based on the calculated discriminant value, the fatty liver disease The result of evaluating the condition,
An information communication terminal device.
A control means and a storage means, which are communicably connected to an information communication terminal device that provides amino acid concentration data to be evaluated regarding the amino acid concentration value, via a network, include fatty liver, NAFLD (non A fatty liver disease evaluation apparatus for evaluating a state of fatty liver disease including at least one of alcoholic fatty liver disease (NAL) and NASH (non-alcoholic steatohepatitis),
The control means includes
Amino acid concentration data receiving means for receiving the amino acid concentration data transmitted from the information communication terminal device;
Based on the amino acid concentration data received by the amino acid concentration data receiving means and the multivariate discriminant stored in the storage means including the amino acid concentration as a variable, a discriminant value that is the value of the multivariate discriminant is calculated. Discriminant value calculating means for
Based on the discriminant value calculated by the discriminant value calculating unit, the discriminant value criterion-evaluating unit that evaluates the state of the fatty liver disease for the evaluation target;
An evaluation result transmitting means for transmitting the evaluation result of the evaluation object in the discriminant value reference evaluating means to the information communication terminal device;
Having
An apparatus for evaluating fatty liver disease.
An acquisition step of acquiring amino acid concentration data relating to a concentration value of an amino acid in blood collected from an evaluation subject to which a desired substance group consisting of one or more substances is administered;
Based on the amino acid concentration data acquired in the acquisition step, the evaluation target includes fatty liver containing at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis). A concentration standard evaluation step for evaluating a disease state;
Based on the evaluation result in the concentration value reference evaluation step, it is determined whether or not the desired substance group prevents the fatty liver disease or improves the state of the fatty liver disease. A determination step;
A method for searching for a substance for preventing / ameliorating fatty liver disease, comprising: